Motivation• Business: Selling products, movies, papers• Research: Baseline for improvements.• Personal uses: • Recommend a song from your own music library

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.

The Amazon Analogy• Product independent.• Product dependent.

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.

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.

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

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.

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.

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. )