Rest api to integrate with your site
- 1. Motivation • Business: Selling products, movies, papers • Research: Baseline for improvements. • Personal uses: • Recommend a song from your own music library
- 2. 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.
- 3. The Amazon Analogy • Product independent. • Product dependent.
- 4. 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.
- 5. 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.
- 6. 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
- 7. 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.
- 8. 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.
- 9. 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. )