#Profile Filtering using AI and ML

[its-vishnu-stuff]

Profile Filtering using AI and ML Services In Hyderabad  – Innodatatics

[beakers-and-telescopes]

Okay, I have my own opinions about AI, especially AI art, but this is actually a very cool application!

So when you think about it, we can quantify vision/sight using the actual wavelengths of light, and we can quantify hearing using frequency, but there really isn't a way to quantify smell. So scientists at the University of Reading set out to create an AI to do just that.

The AI was trained on a dataset of 5000 known odor-causing molecules. It was given their structures, and a list of various scent descriptors (such as "floral sweet" or "musty" or "buttery") and how well those descriptors fit on a scale of 1-5. After being trained on this data, the AI was able to be shown a new molecule and predict what its scent would be, using the various descriptors.

The AI's prediction abilities were compared against a panel of humans, who would smell the compound of interest and assign the descriptors. The AI's predictions were actually just as good as the human descriptions. Professor Jane Parker, who worked on the project, explained the following.

"We don't currently have a way to measure or accurately predict the odor of a molecule, based on its molecular structure. You can get so far with current knowledge of the molecular structure, but eventually you are faced with numerous exceptions where the odor and structure don't match. This is what has stumped previous models of olfaction. The fantastic thing about this new ML generated model is that it correctly predicts the odor of those exceptions"

Now what can we do with this "AI Nose", you might ask? Well, it may have benefits in the food and fragrance industries, for one. A machine that is able to quickly filter through compounds to find one with specific odor qualities could be a good way to find new, sustainable sources of fragrance in foods or perfumes. The team also believes that this "scent map" that the AI model builds could be linked to metabolism. In other words, odors that are close to each other on the map, or smell similar, are also more likely to be metabolically related

[imggloba]

Real Estate Innovation in Dubai: Complete App Development Guide

Dubai's real estate sector is undergoing a dramatic transformation, fueled by rapid technological advancements and the growing demand for digital solutions. From virtual tours and blockchain transactions to AI-driven property recommendations, the real estate market in Dubai is now powered by innovative mobile and web applications. For real estate companies, agents, and investors, building a smart, user-friendly real estate app is no longer a luxury—it's a strategic necessity.

In this complete guide, we’ll break down how to build a powerful real estate app tailored for Dubai’s dynamic market, highlight the latest innovations, and discuss essential features, tech stacks, and development costs. If you’re looking to turn your idea into a profitable app, IMG Global Infotech is your ideal partner, offering end-to-end real estate app development services in Dubai and globally.

Why Dubai is Leading in Real Estate Innovation

Dubai has always positioned itself at the forefront of innovation. Its real estate market mirrors that ambition by embracing:

Smart City initiatives promoting digitization.

A growing expat population seeking efficient property solutions.

High mobile penetration and digital literacy.

Government support for proptech startups.

The result? A booming ecosystem where real estate apps can thrive, provided they are tailored to the region's expectations.

Types of Real Estate Apps Gaining Popularity in Dubai

Before you dive into development, it’s vital to understand the different types of real estate apps making waves in Dubai:

Property Listing Platforms – Apps like Bayut and Property Finder allow users to browse and filter listings by type, price, and location.

Brokerage Management Apps – Used by agents to manage leads, showings, and sales processes.

Rental Apps – Focused solely on long-term and short-term rentals (including holiday rentals).

Virtual Tour Apps – Offer AR/VR-based tours, especially useful for off-plan properties.

Investment Platforms – Cater to real estate investors looking for ROI insights, forecasts, and secure digital transactions.

Core Features for Real Estate Apps in Dubai

To compete in Dubai’s tech-forward environment, your real estate app should include:

Advanced Search Filters (location, type, size, price)

Interactive Maps Integration with nearby amenities

High-Resolution Media Uploads (photos, 360° videos, VR tours)

Multilingual Support (English, Arabic, Russian)

AI-Powered Recommendations based on user behavior

Secure User Authentication & Profiles

In-App Chat with Agents

Real-Time Notifications

Mortgage Calculators

Property Valuation Tools

Admin Dashboard for agents, brokers, or developers

At IMG Global Infotech, we specialize in building feature-rich real estate apps that integrate cutting-edge functionalities while remaining user-friendly and visually stunning.

Tech Stack for Real Estate App Development

Choosing the right technology stack is crucial for building a scalable, secure, and responsive app. Here's a recommended tech stack:

Frontend: React Native or Flutter for cross-platform compatibility

Backend: Node.js or Django for speed and flexibility

Database: PostgreSQL or MongoDB

APIs: Google Maps, payment gateways, CRM integrations

AI/ML Tools: TensorFlow, Dialogflow for smart search and chatbots

AR/VR: Unity or Vuforia for virtual property tours

IMG Global Infotech ensures that the most modern and efficient technologies are selected according to your specific business goals.

Development Stages and Timeline

The process of developing a real estate app typically follows these steps:

Discovery & Planning – Market analysis, competitor benchmarking, and feature outlining (1–2 weeks)

UI/UX Design – Creating user journeys, wireframes, and prototypes (2–3 weeks)

Backend & Frontend Development – Coding core functionalities, APIs, and databases (6–10 weeks)

Testing & QA – Bug fixing, load testing, and performance optimization (2 weeks)

Launch & Deployment – Publishing on iOS and Android stores, post-launch support

Total estimated timeline: 3–4 months, depending on app complexity.

Estimated Cost of Building a Real Estate App in Dubai

Development costs vary based on app features, platforms, and custom integrations. Here’s a general breakdown:

App Type

Estimated Cost (USD)

Basic Property Listing App

$10,000 – $20,000

Advanced Multi-Feature App

$25,000 – $50,000+

AR/VR-Integrated Platform

$50,000 – $80,000+

Working with IMG Global Infotech, you receive transparent pricing, milestone-based billing, and premium-quality development at globally competitive rates.

How Can IMG Global Infotech Help?

IMG Global Infotech stands out as a trusted real estate app development company with:

10+ years of industry experience

A team of certified developers and designers

Proven success in building apps for the Dubai and GCC real estate markets

Commitment to innovation, security, and scalability

End-to-end support from idea validation to post-launch maintenance

Whether you’re a startup, brokerage, or enterprise developer, we build solutions that align with your vision and market needs.

To Wrap It Up

Dubai’s real estate market is ripe for digital disruption, and the right app can give your business a significant competitive edge. From AR-enabled virtual tours to AI-powered property suggestions, today’s innovations are reshaping how people buy, sell, and rent properties in the city.

With a trusted tech partner like IMG Global Infotech, you can turn your real estate app idea into a powerful, revenue-generating product that stands out in Dubai’s digital skyline.

Ready to build your next-gen real estate app? Let’s make it happen.

[damilola-doodles]

📌 Project Title: Movie Recommendation Engine with Time-Decay Weighted User Profiling using Pandas, Surprise Library, and Cosine Similarity.

📁 Filename: movie_recommendation_engine_time_decay.py🔖 Reference ID: ai-ml-ds-MovieRecTD06 🧠 Project Description This project builds a movie recommendation system that goes beyond simple collaborative filtering by incorporating a time-decay factor into user profiles. The idea is that a user’s recent ratings are more indicative of their current preferences than older ratings. It utilizes pandas…

[dammyanimation]

📌 Project Title: Movie Recommendation Engine with Time-Decay Weighted User Profiling using Pandas, Surprise Library, and Cosine Similarity.

📁 Filename: movie_recommendation_engine_time_decay.py🔖 Reference ID: ai-ml-ds-MovieRecTD06 🧠 Project Description This project builds a movie recommendation system that goes beyond simple collaborative filtering by incorporating a time-decay factor into user profiles. The idea is that a user’s recent ratings are more indicative of their current preferences than older ratings. It utilizes pandas…

[damilola-ai-automation]

📌 Project Title: Movie Recommendation Engine with Time-Decay Weighted User Profiling using Pandas, Surprise Library, and Cosine Similarity.

📁 Filename: movie_recommendation_engine_time_decay.py🔖 Reference ID: ai-ml-ds-MovieRecTD06 🧠 Project Description This project builds a movie recommendation system that goes beyond simple collaborative filtering by incorporating a time-decay factor into user profiles. The idea is that a user’s recent ratings are more indicative of their current preferences than older ratings. It utilizes pandas…

[damilola-warrior-mindset]

📌 Project Title: Movie Recommendation Engine with Time-Decay Weighted User Profiling using Pandas, Surprise Library, and Cosine Similarity.

📁 Filename: movie_recommendation_engine_time_decay.py🔖 Reference ID: ai-ml-ds-MovieRecTD06 🧠 Project Description This project builds a movie recommendation system that goes beyond simple collaborative filtering by incorporating a time-decay factor into user profiles. The idea is that a user’s recent ratings are more indicative of their current preferences than older ratings. It utilizes pandas…

[damilola-moyo]

📌 Project Title: Movie Recommendation Engine with Time-Decay Weighted User Profiling using Pandas, Surprise Library, and Cosine Similarity.

📁 Filename: movie_recommendation_engine_time_decay.py🔖 Reference ID: ai-ml-ds-MovieRecTD06 🧠 Project Description This project builds a movie recommendation system that goes beyond simple collaborative filtering by incorporating a time-decay factor into user profiles. The idea is that a user’s recent ratings are more indicative of their current preferences than older ratings. It utilizes pandas…

[photocut-ai]

AI for Artists and Creators: Tattoos, Photos, and Visual Identity Trends

The 2025 giants are currently riding the AI wave, and the possibilities for creativity are endless within the sightline of technology. AI influences tattoo designs that are more intimate and fun and clever, snazzy profile pics, and some killer car pictures. If you're a creator, aspiring designer, or someone looking to glamorize your mithering life, this guide collects the best you could find in 2025. We look at AI websites such as PhotoCut, family-oriented tattoo ideas, solid alternatives to Photoshop, and setting up killer images from profile images to cars with powerful tips. Are you into making serious creative moves? Let us find out how AI and inspiration culminate in forming a unique visual identity for you. 

Improve your images and make them look polished with PhotoCut’s Photo Enhancer.

Best AI Websites to Explore in 2025

AI changes how we communicate with technology, from content to image creation. These are some highlighted AI sites that you would like to visit:

PhotoCut: A practical and streamlined AI photo editor that easily removes backgrounds, modifies colors, and sharpens images in a few taps. 

ChatGPT (OpenAI): A great source for generating content, helping with code, assisting in writing, and creatively brainstorming.

Runway ML: A mixed AI platform dedicated mostly to creative video editing, background removal, and image generation.

Krisp.ai: Eliminates background noise for clear calls and recordings in remote work.

Copy.ai: A go-to tool for crafting marketing copy, product descriptions, and social media posts in seconds. 

These tools make difficult tasks easier and are effective for professionals, students, and creators alike.

Best Family Tattoo Ideas

Family tattoos are more than ink, they’re lifelong symbols of connection. Here are some ideas that beautifully honor your loved ones:

Coordinates or Dates: Capture special moments by inking the coordinates or birthdates of loved ones as a timeless tribute.

Family Tree: A visual or symbolic tree that showcases family lineage and the strength of generational roots.

Linked Hearts and Infinity Symbols: These designs depict everlasting love and unbreakable bonds between family.

Quotes or Phrases: Personal sayings or phrases from the heart, like "Family Over Everything," which is supposed to carry a deep meaning. 

Animal Symbols: An animal, such as wolves, elephants, and lions, that represents loyalty, protection, and unity, makes wonderful family-related tattoo ideas. 

Whether small or big, tattoos are full of feelings, sublime meanings, and timeless significance.

Change your photo backgrounds easily with PhotoCut’s Background Changer.

Best Photoshop Alternatives in 2025

Not everyone needs Photoshop's complexity or cost. These alternatives provide powerful tools without the steep learning curve:

PhotoCut: Quite simple, but an excellent AI-driven tool for removing backgrounds, changing colors, enhancing, and cropping, to make fast, high-quality edits.

Fotor: An online editor that is flexible with countless design tools, effects, and filters for both professional designers and beginners. 

Pixlr: A browser-based photo editor with support for layers and customizable design templates to complement the design process.

Canva: Neatly and elegantly done with drag-and-drop simplicity, unloved by many because of its various inbuilt tools for background removal, graphics, and design layouts. 

GIMP: Rich in features and open-source, and can serve your day if you are interested in taking total creative control and enabling professional-level editing.

These platforms fit everyone from inexperienced users to seasoned designers.

Erase your image backgrounds in seconds with PhotoCut’s Background Eraser.

Best Ideas to Make Your Profile Picture Unique

Your profile photo is your digital handshake, make it count:

Use AI background tools: Utilize PhotoCut to remove or replace backgrounds easily, creating a clean or custom aesthetic.

Add Color or Light Effects: Bring soft colors, flares, or gradients to lend a more modern, eye-catching look.

Angle Play and Close Show: A point of view may make a feature more fun and stunning for one's best feature.

Pets, Props, and Hobbies: Infuse your personality into something remarkable in your profile picture.

Seasonal or Themed Versions: Keep it updated with fresh profile changes for more seasonal, holiday, or mindset types throughout the year.

Use PhotoCut to polish the lighting, blur backgrounds, and crop it to achieve that frame.

Explore apps to remove image backgrounds effortlessly for free.

Best Tips and Tricks for Car Photography

Want car photos that turn heads? These techniques will elevate your automotive shots:

Shoot at Golden Hour: Capture that warm, natural light after sunrise or before sunset for amazing car shots.

Reflections: Another good way to use them is to put the automobile where its lines can show off and stay clear of distracting background clutter.

Dynamism in Angles: Get as low as possible to the ground and shoot through objects.

Attention to Detail: Draw in focus on fine details like emblems, wheels, or interior to give a more multifaceted picture.

Edit with PhotoCut: Use the tool to blur any distracting background, enhance lighting and color, or even change the scene for a well-polished, powerful final image.

Combining smart photography with AI editing creates showroom-quality results, even with your phone.

Conclusion

Creativity comes easily with AI tools when planning a mindful family tattoo, remodeling the ideal profile picture, or taking beautiful shots of cars. Such powerful editing is conveniently available with smart platforms like PhotoCut. Background can be removed or changed, enhancement added, effects given, or ideal visions made without any design experience. This blog contains the best ideas and tools to help anyone, from beginners to professionals, make magnificent visuals. Cutting it from polished edits through personalized designs is all about transforming inspiration into something shareworthy with a few taps. Share your creativity, use PhotoCut and other AI tools to get it simple, fast, and fun.

Elevate your photos by adding a baby blue background using PhotoCut.

FAQs

Q1. What are some of the top AI websites that one should explore? 

Ans. Some well-known websites that provide AI resources include OpenAI, Google AI Experiments, IBM on its Watson platform, and Microsoft AI School, which offer a wealth of AI-related apps and resources, as well as programs to study and research.

Q2. How would you search for any AI site that matches your interest? 

Ans. Try searching for AI websites with some keywords like "AI for healthcare," "AI on Finance," or "AI and Education." Also, you could look for AI directories or AITime and AI Startup List, which organize websites and resources related to AI for an industry or topic.

Q3. What are some popular family tattoo ideas? 

Ans. Some of the popular tattoo ideas for families could include family trees, interlocking hands or puzzle piece designs, sayings or quotes meant for the family, or other potent symbols like birds, anchors, or hearts. Even more special would be the addition of personal touches, such as birth dates, initials, or significant moments, to the overall tattoo concept.

Q4. What do I consider to find a suitable family tattoo design for my family? 

Ans. Feelings about the personality and interests of your family, values, etc., must play an important part while considering a family tattoo design. You should involve other family members in deciding by asking for their comments or suggestions. Consider size, placement, and style of the design, along with any cultural or symbolic references that may arise.

Q5. What other programs can be used for photo editing besides Photoshop? 

Ans. Apart from Affinity Photo, GIMP, Canva, and Pixlr, these are the best photo editing programs which are great alternatives to Photoshop. They include everything from retouching and filters to complete layering and masking for image editing and improvement.

Q6. How do I find my definite Photoshop alternative? 

Ans. To find which alternative to Photoshop works best for you, consider your budget, level of experience, and the exact needs for your work. Beyond that, try as many as you can! Compare their features, how they perform, and how easy they are to operate. Most Photoshop alternatives have trial versions or demos, which will help you a lot in finding out whether the alternative presents what you require best.

Q7. What are the techniques for personalizing and expressing uniqueness in a profile picture? 

Ans. Customized filters, special effects, and graphic additions; borders or frames customized; taking pictures in various perspectives or angles; integrating text or typography; and using positive space in unorthodox or surprising ways, with overlays or collage- all of this is purposely infrastructure tied-in differently.

Q8. How do I ensure my profile picture makes me look good? 

Ans. Positive, accurate portrayals do require one to ask who will see the image, what the use will be, and what it captures. When that is done, you can ask for people's feedback on it. And finally, make sure the photograph is bright, clear, and true to you, including your personality, style, and core values.

Q9. What are some tips and tricks for taking amazing car photography? 

Ans. To shoot the perfect car photographs, the best advice would include searching for some interesting places, using tripods and remote shutter cables, shooting early during the golden hour or blue hour, capturing from various angles possible, shooting with a very low aperture for shallow depth of field, and post-processing for color contrast and sharpness adjustments.

Q10. How can I improve my car photography skills and techniques? 

Ans. Experimentation with various approaches and equipment options, as well as settings, is the best way to learn to photograph cars. Learning can also take place from analysis and research of other photographers' work, seminars, classes, and involvement in online forums or groups. Get the best equipment and accessories, lenses, filters, reflectors, and so such to have the best use of your photography shots.

[its-vishnu-stuff]

Profile Filtering using AI and ML  –   Innodatatics

Using state-of-the-art algorithms and machine learning models, profile filtering with AI and ML analyzes and categorizes user profiles based on various characteristics and preferences.

[jcmarchi]

Using AI to Predict a Blockbuster Movie

New Post has been published on https://thedigitalinsider.com/using-ai-to-predict-a-blockbuster-movie/

Using AI to Predict a Blockbuster Movie

Although film and television are often seen as creative and open-ended industries, they have long been risk-averse. High production costs (which may soon lose the offsetting advantage of cheaper overseas locations, at least for US projects) and a fragmented production landscape make it difficult for independent companies to absorb a significant loss.

Therefore, over the past decade, the industry has taken a growing interest in whether machine learning can detect trends or patterns in how audiences respond to proposed film and television projects.

The main data sources remain the Nielsen system (which offers scale, though its roots lie in TV and advertising) and sample-based methods such as focus groups, which trade scale for curated demographics. This latter category also includes scorecard feedback from free movie previews – however, by that point, most of a production’s budget is already spent.

The ‘Big Hit’ Theory/Theories

Initially, ML systems leveraged traditional analysis methods such as linear regression, K-Nearest Neighbors, Stochastic Gradient Descent, Decision Tree and Forests, and Neural Networks, usually in various combinations nearer in style to pre-AI statistical analysis, such as a 2019 University of Central Florida initiative to forecast successful TV shows based on combinations of actors and writers (among other factors):

A 2018 study rated the performance of episodes based on combinations of characters and/or writer (most episodes were written by more than one person). Source: https://arxiv.org/pdf/1910.12589

The most relevant related work, at least that which is deployed in the wild (though often criticized) is in the field of recommender systems:

A typical video recommendation pipeline. Videos in the catalog are indexed using features that may be manually annotated or automatically extracted. Recommendations are generated in two stages by first selecting candidate videos and then ranking them according to a user profile inferred from viewing preferences. Source: https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2023.1281614/full

However, these kinds of approaches analyze projects that are already successful. In the case of prospective new shows or movies, it is not clear what kind of ground truth would be most applicable – not least because changes in public taste, combined with improvements and augmentations of data sources, mean that decades of consistent data is usually not available.

This is an instance of the cold start problem, where recommendation systems must evaluate candidates without any prior interaction data. In such cases, traditional collaborative filtering breaks down, because it relies on patterns in user behavior (such as viewing, rating, or sharing) to generate predictions. The problem is that in the case of most new movies or shows, there is not yet enough audience feedback to support these methods.

Comcast Predicts

A new paper from Comcast Technology AI, in association with George Washington University, proposes a solution to this problem by prompting a language model with structured metadata about unreleased movies.

The inputs include cast, genre, synopsis, content rating, mood, and awards, with the model returning a ranked list of likely future hits.

The authors use the model’s output as a stand-in for audience interest when no engagement data is available, hoping to avoid early bias toward titles that are already well known.

The very short (three-page) paper, titled Predicting Movie Hits Before They Happen with LLMs, comes from six researchers at Comcast Technology AI, and one from GWU, and states:

‘Our results show that LLMs, when using movie metadata, can significantly outperform the baselines. This approach could serve as an assisted system for multiple use cases, enabling the automatic scoring of large volumes of new content released daily and weekly.

‘By providing early insights before editorial teams or algorithms have accumulated sufficient interaction data, LLMs can streamline the content review process.

‘With continuous improvements in LLM efficiency and the rise of recommendation agents, the insights from this work are valuable and adaptable to a wide range of domains.’

If the approach proves robust, it could reduce the industry’s reliance on retrospective metrics and heavily-promoted titles by introducing a scalable way to flag promising content prior to release. Thus, rather than waiting for user behavior to signal demand, editorial teams could receive early, metadata-driven forecasts of audience interest, potentially redistributing exposure across a wider range of new releases.

Method and Data

The authors outline a four-stage workflow: construction of a dedicated dataset from unreleased movie metadata; the establishment of a baseline model for comparison; the evaluation of apposite LLMs using both natural language reasoning and embedding-based prediction; and the optimization of outputs through prompt engineering in generative mode, using Meta’s Llama 3.1 and 3.3 language models.

Since, the authors state, no publicly available dataset offered a direct way to test their hypothesis (because most existing collections predate LLMs, and lack detailed metadata), they built a benchmark dataset from the Comcast entertainment platform, which serves tens of millions of users across direct and third-party interfaces.

The dataset tracks newly-released movies, and whether they later became popular, with popularity defined through user interactions.

The collection focuses on movies rather than series, and the authors state:

‘We focused on movies because they are less influenced by external knowledge than TV series, improving the reliability of experiments.’

Labels were assigned by analyzing the time it took for a title to become popular across different time windows and list sizes. The LLM was prompted with metadata fields such as genre, synopsis, rating, era, cast, crew, mood, awards, and character types.

For comparison, the authors used two baselines: a random ordering; and a Popular Embedding (PE) model (which we will come to shortly).

The project used large language models as the primary ranking method, generating ordered lists of movies with predicted popularity scores and accompanying justifications – and these outputs were shaped by prompt engineering strategies designed to guide the model’s predictions using structured metadata.

The prompting strategy framed the model as an ‘editorial assistant’ assigned with identifying which upcoming movies were most likely to become popular, based solely on structured metadata, and then tasked with reordering a fixed list of titles without introducing new items, and to return the output in JSON format.

Each response consisted of a ranked list, assigned popularity scores, justifications for the rankings, and references to any prior examples that influenced the outcome. These multiple levels of metadata were intended to improve the model’s contextual grasp, and its ability to anticipate future audience trends.

Tests

The experiment followed two main stages: initially, the authors tested several model variants to establish a baseline, involving the identification of the version which performed better than a random-ordering approach.

Second, they tested large language models in generative mode, by comparing their output to a stronger baseline, rather than a random ranking, raising the difficulty of the task.

This meant the models had to do better than a system that already showed some ability to predict which movies would become popular. As a result, the authors assert, the evaluation better reflected real-world conditions, where editorial teams and recommender systems are rarely choosing between a model and chance, but between competing systems with varying levels of predictive ability.

The Advantage of Ignorance

A key constraint in this setup was the time gap between the models’ knowledge cutoff and the actual release dates of the movies. Because the language models were trained on data that ended six to twelve months before the movies became available, they had no access to post-release information, ensuring that the predictions were based entirely on metadata, and not on any learned audience response.

Baseline Evaluation

To construct a baseline, the authors generated semantic representations of movie metadata using three embedding models: BERT V4; Linq-Embed-Mistral 7B; and Llama 3.3 70B, quantized to 8-bit precision to meet the constraints of the experimental environment.

Linq-Embed-Mistral was selected for inclusion due to its top position on the MTEB (Massive Text Embedding Benchmark) leaderboard.

Each model produced vector embeddings of candidate movies, which were then compared to the average embedding of the top one hundred most popular titles from the weeks preceding each movie’s release.

Popularity was inferred using cosine similarity between these embeddings, with higher similarity scores indicating higher predicted appeal. The ranking accuracy of each model was evaluated by measuring performance against a random ordering baseline.

Performance improvement of Popular Embedding models compared to a random baseline. Each model was tested using four metadata configurations: V1 includes only genre; V2 includes only synopsis; V3 combines genre, synopsis, content rating, character types, mood, and release era; V4 adds cast, crew, and awards to the V3 configuration. Results show how richer metadata inputs affect ranking accuracy. Source: https://arxiv.org/pdf/2505.02693

The results (shown above), demonstrate that BERT V4 and Linq-Embed-Mistral 7B delivered the strongest improvements in identifying the top three most popular titles, although both fell slightly short in predicting the single most popular item.

BERT was ultimately selected as the baseline model for comparison with the LLMs, as its efficiency and overall gains outweighed its limitations.

LLM Evaluation

The researchers assessed performance using two ranking approaches: pairwise and listwise. Pairwise ranking evaluates whether the model correctly orders one item relative to another; and listwise ranking considers the accuracy of the entire ordered list of candidates.

This combination made it possible to evaluate not only whether individual movie pairs were ranked correctly (local accuracy), but also how well the full list of candidates reflected the true popularity order (global accuracy).

Full, non-quantized models were employed to prevent performance loss, ensuring a consistent and reproducible comparison between LLM-based predictions and embedding-based baselines.

Metrics

To assess how effectively the language models predicted movie popularity, both ranking-based and classification-based metrics were used, with particular attention to identifying the top three most popular titles.

Four metrics were applied: Accuracy@1 measured how often the most popular item appeared in the first position; Reciprocal Rank captured how high the top actual item ranked in the predicted list by taking the inverse of its position; Normalized Discounted Cumulative Gain (NDCG@k) evaluated how well the entire ranking matched actual popularity, with higher scores indicating better alignment; and Recall@3 measured the proportion of truly popular titles that appeared in the model’s top three predictions.

Since most user engagement happens near the top of ranked menus, the evaluation focused on lower values of k, to reflect practical use cases.

Performance improvement of large language models over BERT V4, measured as percentage gains across ranking metrics. Results were averaged over ten runs per model-prompt combination, with the top two values highlighted. Reported figures reflect the average percentage improvement across all metrics.

The performance of Llama model 3.1 (8B), 3.1 (405B), and 3.3 (70B) was evaluated by measuring metric improvements relative to the earlier-established BERT V4 baseline. Each model was tested using a series of prompts, ranging from minimal to information-rich, to examine the effect of input detail on prediction quality.

The authors state:

‘The best performance is achieved when using Llama 3.1 (405B) with the most informative prompt, followed by Llama 3.3 (70B). Based on the observed trend, when using a complex and lengthy prompt (MD V4), a more complex language model generally leads to improved performance across various metrics. However, it is sensitive to the type of information added.’

Performance improved when cast awards were included as part of the prompt – in this case, the number of major awards received by the top five billed actors in each film. This richer metadata was part of the most detailed prompt configuration, outperforming a simpler version that excluded cast recognition. The benefit was most evident in the larger models, Llama 3.1 (405B) and 3.3 (70B), both of which showed stronger predictive accuracy when given this additional signal of prestige and audience familiarity.

By contrast, the smallest model, Llama 3.1 (8B), showed improved performance as prompts became slightly more detailed, progressing from genre to synopsis, but declined when more fields were added, suggesting that the model lacked the capacity to integrate complex prompts effectively, leading to weaker generalization.

When prompts were restricted to genre alone, all models under-performed against the baseline, demonstrating that limited metadata was insufficient to support meaningful predictions.

Conclusion

LLMs have become the poster child for generative AI, which might explain why they’re being put to work in areas where other methods could be a better fit. Even so, there’s still a lot we don’t know about what they can do across different industries, so it makes sense to give them a shot.

In this particular case, as with stock markets and weather forecasting, there is only a limited extent to which historical data can serve as the foundation of future predictions. In the case of movies and TV shows, the very delivery method is now a moving target, in contrast to the period between 1978-2011, when cable, satellite and portable media (VHS, DVD, et al.) represented a series of transitory or evolving historical disruptions.

Neither can any prediction method account for the extent to which the success or failure of other productions may influence the viability of a proposed property – and yet this is frequently the case in the movie and TV industry, which loves to ride a trend.

Nonetheless, when used thoughtfully, LLMs could help strengthen recommendation systems during the cold-start phase, offering useful support across a range of predictive methods.

First published Tuesday, May 6, 2025

[imggloba]

Real Estate Innovation in Dubai: Complete App Development Guide

Dubai's real estate sector is undergoing a dramatic transformation, fueled by rapid technological advancements and the growing demand for digital solutions. From virtual tours and blockchain transactions to AI-driven property recommendations, the real estate market in Dubai is now powered by innovative mobile and web applications. For real estate companies, agents, and investors, building a smart, user-friendly real estate app is no longer a luxury—it's a strategic necessity.

In this complete guide, we’ll break down how to build a powerful real estate app tailored for Dubai’s dynamic market, highlight the latest innovations, and discuss essential features, tech stacks, and development costs. If you’re looking to turn your idea into a profitable app, IMG Global Infotech is your ideal partner, offering end-to-end real estate app development services in Dubai and globally.

Why Dubai is Leading in Real Estate Innovation

Dubai has always positioned itself at the forefront of innovation. Its real estate market mirrors that ambition by embracing:

Smart City initiatives promoting digitization.

A growing expat population seeking efficient property solutions.

High mobile penetration and digital literacy.

Government support for proptech startups.

The result? A booming ecosystem where real estate apps can thrive, provided they are tailored to the region's expectations.

Types of Real Estate Apps Gaining Popularity in Dubai

Before you dive into development, it’s vital to understand the different types of real estate apps making waves in Dubai:

Property Listing Platforms – Apps like Bayut and Property Finder allow users to browse and filter listings by type, price, and location.

Brokerage Management Apps – Used by agents to manage leads, showings, and sales processes.

Rental Apps – Focused solely on long-term and short-term rentals (including holiday rentals).

Virtual Tour Apps – Offer AR/VR-based tours, especially useful for off-plan properties.

Investment Platforms – Cater to real estate investors looking for ROI insights, forecasts, and secure digital transactions.

Core Features for Real Estate Apps in Dubai

To compete in Dubai’s tech-forward environment, your real estate app should include:

Advanced Search Filters (location, type, size, price)

Interactive Maps Integration with nearby amenities

High-Resolution Media Uploads (photos, 360° videos, VR tours)

Multilingual Support (English, Arabic, Russian)

AI-Powered Recommendations based on user behavior

Secure User Authentication & Profiles

In-App Chat with Agents

Real-Time Notifications

Mortgage Calculators

Property Valuation Tools

Admin Dashboard for agents, brokers, or developers

At IMG Global Infotech, we specialize in building feature-rich real estate apps that integrate cutting-edge functionalities while remaining user-friendly and visually stunning.

Tech Stack for Real Estate App Development

Choosing the right technology stack is crucial for building a scalable, secure, and responsive app. Here's a recommended tech stack:

Frontend: React Native or Flutter for cross-platform compatibility

Backend: Node.js or Django for speed and flexibility

Database: PostgreSQL or MongoDB

APIs: Google Maps, payment gateways, CRM integrations

AI/ML Tools: TensorFlow, Dialogflow for smart search and chatbots

AR/VR: Unity or Vuforia for virtual property tours

IMG Global Infotech ensures that the most modern and efficient technologies are selected according to your specific business goals.

Development Stages and Timeline

The process of developing a real estate app typically follows these steps:

Discovery & Planning – Market analysis, competitor benchmarking, and feature outlining (1–2 weeks)

UI/UX Design – Creating user journeys, wireframes, and prototypes (2–3 weeks)

Backend & Frontend Development – Coding core functionalities, APIs, and databases (6–10 weeks)

Testing & QA – Bug fixing, load testing, and performance optimization (2 weeks)

Launch & Deployment – Publishing on iOS and Android stores, post-launch support

Total estimated timeline: 3–4 months, depending on app complexity.

Estimated Cost of Building a Real Estate App in Dubai

Development costs vary based on app features, platforms, and custom integrations. Here’s a general breakdown:

App Type

Estimated Cost (USD)

Basic Property Listing App

$10,000 – $20,000

Advanced Multi-Feature App

$25,000 – $50,000+

AR/VR-Integrated Platform

$50,000 – $80,000+

Working with IMG Global Infotech, you receive transparent pricing, milestone-based billing, and premium-quality development at globally competitive rates.

How Can IMG Global Infotech Help?

IMG Global Infotech stands out as a trusted real estate app development company with:

10+ years of industry experience

A team of certified developers and designers

Proven success in building apps for the Dubai and GCC real estate markets

Commitment to innovation, security, and scalability

End-to-end support from idea validation to post-launch maintenance

Whether you’re a startup, brokerage, or enterprise developer, we build solutions that align with your vision and market needs.

To Wrap It Up

Dubai’s real estate market is ripe for digital disruption, and the right app can give your business a significant competitive edge. From AR-enabled virtual tours to AI-powered property suggestions, today’s innovations are reshaping how people buy, sell, and rent properties in the city.

With a trusted tech partner like IMG Global Infotech, you can turn your real estate app idea into a powerful, revenue-generating product that stands out in Dubai’s digital skyline.

Ready to build your next-gen real estate app? Let’s make it happen.

[damilola-doodles]

📌 Project Title: Movie Recommendation Engine with Time-Decay Weighted User Profiling using Pandas, Surprise Library, and Cosine Similarity.

📁 Filename: movie_recommendation_engine_time_decay.py🔖 Reference ID: ai-ml-ds-MovieRecTD06 🧠 Project Description This project builds a movie recommendation system that goes beyond simple collaborative filtering by incorporating a time-decay factor into user profiles. The idea is that a user’s recent ratings are more indicative of their current preferences than older ratings. It utilizes pandas…

[dammyanimation]

📌 Project Title: Movie Recommendation Engine with Time-Decay Weighted User Profiling using Pandas, Surprise Library, and Cosine Similarity.

📁 Filename: movie_recommendation_engine_time_decay.py🔖 Reference ID: ai-ml-ds-MovieRecTD06 🧠 Project Description This project builds a movie recommendation system that goes beyond simple collaborative filtering by incorporating a time-decay factor into user profiles. The idea is that a user’s recent ratings are more indicative of their current preferences than older ratings. It utilizes pandas…

[damilola-ai-automation]

📌 Project Title: Movie Recommendation Engine with Time-Decay Weighted User Profiling using Pandas, Surprise Library, and Cosine Similarity.

📁 Filename: movie_recommendation_engine_time_decay.py🔖 Reference ID: ai-ml-ds-MovieRecTD06 🧠 Project Description This project builds a movie recommendation system that goes beyond simple collaborative filtering by incorporating a time-decay factor into user profiles. The idea is that a user’s recent ratings are more indicative of their current preferences than older ratings. It utilizes pandas…

[damilola-warrior-mindset]

📌 Project Title: Movie Recommendation Engine with Time-Decay Weighted User Profiling using Pandas, Surprise Library, and Cosine Similarity.

📁 Filename: movie_recommendation_engine_time_decay.py🔖 Reference ID: ai-ml-ds-MovieRecTD06 🧠 Project Description This project builds a movie recommendation system that goes beyond simple collaborative filtering by incorporating a time-decay factor into user profiles. The idea is that a user’s recent ratings are more indicative of their current preferences than older ratings. It utilizes pandas…