A hybrid explanations framework for collaborative filtering recommender systems

Shay Ben-Elazar, Noam Koenigstein

Research output: Contribution to journalConference articlepeer-review


Augmenting personalized recommendations with explanations is believed to improve users' trust, loyalty, satisfaction, and recommender's persuasiveness. We present a flexible explanations framework for collaborative filtering recom-mender systems. Our algorithms utilizes item tags to automatically generate personalized explanations in a natural language format. Given a specific user and a recommended item, the algorithm utilizes the user's personal information as well as global information (e.g., item similarities, metadata) in order to rank item tags based on their "explanatory power". The top tags are chosen to construct a personalized explanation sentence which helps shed light on the underlying recommender. Our system has been well received by both focus groups as well as in expert evaluations and is scheduled to be evaluated in an online experiment.

Original languageEnglish
JournalCEUR Workshop Proceedings
StatePublished - 2014
Externally publishedYes
Event8th ACM Conference on Recommender Systems, RecSys 2014 - Foster City, United States
Duration: 6 Oct 201410 Oct 2014

All Science Journal Classification (ASJC) codes

  • General Computer Science


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