Domain ranking for cross domain collaborative filtering

Amit Tiroshi, Tsvi Kuflik

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

Abstract

In recommendation systems a variation of the cold start problem is a situation where the target user has few-to-none item ratings belonging to the target domain (e.g., movies) to base recommendations on. One way to overcome this is by basing recommendations on items from different domains, for example recommending movies based on the target user's book item ratings. This technique is called cross-domain recommendation. When basing recommendations on a source domain that is different from the target domain a question arises, from which domain should items be chosen? Is there a source domain that is a better predictor for each target domain? Do books better predict a users' taste in movies or perhaps it's their music preferences? In this study we present initial results of work in progress that ranks and maps between pairs of domains based on the ability to create recommendations in domain one using ratings of items from the other domain. The recommendations are made using cross domain collaborative filtering, and evaluated on the social networking profiles of 2148 users. Initial results show that information that is freely available in social networks can be used for cross domain recommendation and that there are differences between the source domains with respect to the quality of the recommendations.

Original languageAmerican English
Title of host publicationUser Modeling, Adaptation, and Personalization - 20th International Conference, UMAP 2012, Proceedings
Pages328-333
Number of pages6
DOIs
StatePublished - 2012
Event20th International Conference on User Modeling, Adaptation and Personalization, UMAP 2012 - Montreal, QC, Canada
Duration: 16 Jul 201220 Jul 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7379 LNCS

Conference

Conference20th International Conference on User Modeling, Adaptation and Personalization, UMAP 2012
Country/TerritoryCanada
CityMontreal, QC
Period16/07/1220/07/12

Keywords

  • cold-start problem
  • collaborativefiltering
  • cross-domain recommendation

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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