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The technical tagteam behind this blog. We aim to showcase the latest research, tools, and hardware for developing AI applications.

Movie Trailer Similarity for Recommendation

Intro In a previous post, we discussed scraping a movie poster image corpus with genre labels from imdb and learning image similarity models using tensorflow. In this post, we extend this idea to recommend movie trailers based on audio-visual similarity. Data We started by scraping IMDB for movie trailers and their genre tags as labels. Using Scrapy, it is easy to build a text file of video links to then download with youtube-dl....

 路 4 min 路 Terry Rodriguez & Salma Mayorquin

Scraping Smarter with Content Filtering

Scrapy is a powerful web scraping framework and essential tool for building machine learning datasets. When a site has a particularly simple structure, scrapy makes it easy to get a spider running to build up a curated dataset. Check out the tutorials in scrapy鈥檚 documentation. For example, to train a poster similarity model, we first needed to gather many movie posters. Consider trying to scrape IMDb.com.We may be interested in gathering posters from <img> tags under <div> tags with the class "poster"....

 路 3 min 路 Terry Rodriguez & Salma Mayorquin

Movie Poster Similarity for Recommendation

This year marks a sharp increase in the use of streaming services. Many platforms use video posters as the main representation of content to watch. Naturally, the visual representation strongly influences a user鈥檚 propensity to watch the title. In fact, posters are designed to signal theme, genre and era. There are many theories on how poster elements can convey an emotion or capture attention. Netflix conducted a UX study, using eye tracking to find that 91% of titles are rejected after roughly 1 second of view time....

 路 3 min 路 Terry Rodriguez & Salma Mayorquin

TF-Recommenders & Kubernetes for flexible RecSys Model Development & Deployment

Introducing TF-Recommenders Recently, Google open sourced a Keras API for building recommender systems called TF-Recommenders. TF-Recommenders is flexible, making it easy to integrate heterogeneous signals like implicit ratings from user interactions, content embeddings, or real-time context info. This module also introduces losses specialized for ranking and retrieval which can be combined to benefit from multi-task learning. The developers emphasize the ease-of-use in research, as well as the robustness for deployment in web-scale applications....

 路 5 min 路 Terry Rodriguez & Salma Mayorquin

TF-Ranking and BERT for Movie Recommendations

Check out our repo for all the code referenced in this blog! Recommender systems are used by many groups to maximize the presentation of products to users. There is a variety of implementations for building recommender systems, but at their core, these systems are designed to sort a universe of items by their relevance to a user based on user information, item information, or both. One well known algorithm for solving the sorting problem is the Learn-to-Rank model, where the objective is to rank a list of examples by each item鈥檚 relevance to a particular user....

 路 6 min 路 Terry Rodriguez & Salma Mayorquin