Transfer learning for content-based recommender systems using tree matching

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

7 Scopus citations

Abstract

In this paper we present a new approach to content-based transfer learning for solving the data sparsity problem in cases when the users' preferences in the target domain are either scarce or unavailable, but the necessary information for the preferences exists in another domain. Training a system to use such information across domains is shown to produce better performance. Specifically, we represent users' behavior patterns based on topological graph structures. Each behavior pattern represents the behavior of a set of users, when the users' behavior is defined as the items they rated and the items' rating values. In the next step, a correlation is found between behavior patterns in the source domain and target domain. This mapping is considered a bridge between the two. Based on the correlation and content-attributes of the items, a machine learning model is trained to predict users' ratings in the target domain. When our approach is compared to the popularity approach and KNN-cross-domain on a real world dataset, the results show that our approach outperforms both methods on an average of 83%.

Original languageEnglish
Title of host publicationAvailability, Reliability, and Security in Information Systems and HCI - IFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2013, Proceedings
Pages387-399
Number of pages13
DOIs
StatePublished - 22 Oct 2013
EventIFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference on Availability, Reliability, and Security in Information Systems and HCI, CD-ARES 2013 - Regensburg, Germany
Duration: 2 Sep 20136 Sep 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8127 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceIFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference on Availability, Reliability, and Security in Information Systems and HCI, CD-ARES 2013
Country/TerritoryGermany
CityRegensburg
Period2/09/136/09/13

Keywords

  • Behavior Patterns
  • Content-based
  • Recommender-Systems
  • Transfer Learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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