Rest api to integrate with your site

Download Rest api to integrate with your site

Post on 23-Feb-2017

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Open Source Recommender System

MotivationBusiness: Selling products, movies, papersResearch: Baseline for improvements.Personal uses: Recommend a song from your own music library

Input and OutputsTwo relations: User and ProductsInput: User actionsBuyViewRateCan there be any other actions?Output: Product suggestionsOther users also viewed/bought/rated good.What are the best products for this user?Search engine analogy.

The Amazon AnalogyProduct independent.Product dependent.

Easyrec: Open Source Recommender Enginehttp://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:Dont 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 StartedCreate 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 DataInput 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 actionSample API calls:http://easyrec.sourceforge.net/wiki/index.php?title=REST_API_v0.98

API: Get RecommendationsOther 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 RankingRules: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.

ConclusionAn 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. )