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Open Source Recommender System

Open Source Recommender System

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Open Source Recommender System

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  1. Open Source Recommender System

    Sagnik Ray Choudhury
  2. Motivation Business: Selling products, movies, papers Research: Baseline for improvements. Personal uses: Recommend a song from your own music library
  3. Input and Outputs Two relations: User and Products Input: User actions Buy View Rate Can there be any other actions? Output: Product suggestions Other users also viewed/bought/rated good. What are the best products for this user? Search engine analogy.
  4. The Amazon Analogy Product independent. Product dependent.
  5. Easyrec: Open Source Recommender Engine http://www.easyrec.org/ Can be used in two ways: Download a copy and run in localhost. Use the easyrec server. Use the REST API to integrate with your site. API pros and cons: Don’t need to bother about computation power. Very easy for prototyping. Data privacy (if you are running on easyrec server). Not flexible enough for fine grained customization.
  6. API: Getting Started Create an user account, get a token. Create a “tenant id”: url for your website/your home computer/anything. “tenant-id” and token combined work as a primary key. Any call to the easyrec must contain these two parameters.
  7. API: Input Your Data Input options: View: The user has viewed this item. Buy: The user has bought this item. Rate: The user has rated this item. You can also define your own “action” Sample API calls: http://easyrec.sourceforge.net/wiki/index.php?title=REST_API_v0.98
  8. API: Get Recommendations Other users also viewed: Parameter: item id. Other users also bought: Parameter: item id. Items rated good by other users: Parameter: item id. Users who bought this item rated these items good. Recommendations for user: Parameter: user id.
  9. API: Rules, Clustering and Community Ranking Rules: You can write your own rules which will associate two items (users who rated item A high, also bought item B). Rules can not be written between an user and an item. Clustering: You can create clusters of items (laptop/books/songs) You can get all items in a cluster. Community ranking: Items liked by/bought by/ rated by most users.
  10. Conclusion An open source recommender system which can be readily deployed in a small e-commerce site. Not much flexible: You might want to recommend items in a cluster based on user history on that cluster. You want to develop a separate ranking function. Only collaborative filtering: no content based recommendation. Real sites have used this: http://www.flimmit.com. (See a recommendation here. )