Selecting content-based features for collaborative filtering recommenders

Royi Ronen, Noam Koenigstein, Elad Ziklik, Nir Nice

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We study the problem of scoring and selecting content-based features for a collaborative filtering (CF) recommender system. Content-based features play a central role in mitigating the "cold start problem in commercial recommenders. They are also useful in other related tasks, such as recommendation explanation and visualization. However, traditional feature selection methods do not generalize well to recommender systems. As a result, commercial systems typically use manually crafted and selected features. This work presents a framework for automated selection of informative content-based features, that is independent of the type of recommender system or the type of features. We evaluate on recommenders from different domains: books, movies and smart-phone apps, and show effective results on each. In addition, we show how to use the proposed methods to generate meaningful features from text.

Original languageEnglish
Title of host publicationRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems
Pages407-410
Number of pages4
DOIs
StatePublished - 2013
Externally publishedYes
Event7th ACM Conference on Recommender Systems, RecSys 2013 - Hong Kong, China
Duration: 12 Oct 201316 Oct 2013

Publication series

NameRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems

Conference

Conference7th ACM Conference on Recommender Systems, RecSys 2013
Country/TerritoryChina
CityHong Kong
Period12/10/1316/10/13

Keywords

  • Cold start
  • Collaborative filtering
  • Content based
  • Feature selection
  • Recommender systems

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