How do you deal with the cold start problem for new movies in content-based filtering?

1 Use metadata

One way to deal with the cold start problem for new movies is to use metadata, which is information about the movies that is not derived from user ratings or reviews. Metadata can include the release date, the production company, the budget, the box office, the awards, the language, the country, and the ratings from external sources, such as IMDb or Rotten Tomatoes. Metadata can help you enrich the feature vectors of the movies and capture their similarities and differences based on objective criteria. For example, you can use metadata to filter out movies that are too old, too low-budget, or too poorly rated for your target audience.

2 Use content analysis

Another way to deal with the cold start problem for new movies is to use content analysis, which is a technique that extracts meaningful information from the textual or visual content of the movies. Content analysis can include natural language processing, image processing, video processing, and audio processing. Content analysis can help you generate more relevant and diverse features for the movies and measure their similarity and dissimilarity based on subjective criteria. For example, you can use content analysis to extract keywords, topics, sentiments, emotions, styles, genres, themes, and plots from the movie descriptions, posters, trailers, and soundtracks.

3 Use hybrid filtering

A third way to deal with the cold start problem for new movies is to use hybrid filtering, which is a technique that combines content-based filtering with other types of recommender systems, such as collaborative filtering, knowledge-based filtering, or social filtering. Hybrid filtering can help you leverage the advantages of different approaches and overcome their limitations. For example, you can use collaborative filtering to incorporate user preferences and feedback, knowledge-based filtering to incorporate domain knowledge and rules, or social filtering to incorporate social network and influence. By using hybrid filtering, you can improve the accuracy and diversity of the recommendations and reduce the dependency on user ratings or reviews.

4 Use feedback loops

A fourth way to deal with the cold start problem for new movies is to use feedback loops, which are mechanisms that collect and use user responses to the recommendations to update and improve the system. Feedback loops can include explicit feedback, such as ratings, reviews, likes, dislikes, or comments, or implicit feedback, such as clicks, views, purchases, or skips. Feedback loops can help you learn from the user behavior and preferences and adjust the feature weights and similarity measures accordingly. For example, you can use feedback loops to reward movies that receive positive feedback and penalize movies that receive negative feedback or are ignored.

5 Use exploration-exploitation trade-off

A fifth way to deal with the cold start problem for new movies is to use exploration-exploitation trade-off, which is a principle that balances the need to explore new and unknown options with the need to exploit existing and known options. Exploration-exploitation trade-off can help you optimize the performance and satisfaction of the system and the user over time. For example, you can use exploration-exploitation trade-off to allocate a portion of the recommendations to new movies that have high potential but low certainty and another portion to old movies that have low potential but high certainty. By doing so, you can increase the exposure and discovery of new movies and reduce the risk and boredom of old movies.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?