This document discusses Easyrec, an open source recommender engine that provides recommendations based on user actions like views, purchases, and ratings of products. It has an API that allows integrating recommendations into a website by passing a user ID and token. The API can take in user data and return recommendations based on what other similar users viewed, purchased, or rated highly. While Easyrec is easy to use, it has limitations in flexibility and only supports collaborative filtering recommendations.

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. )