A user re-modeling approach to item recommendation using complex usage data

Oren Sar Shalom, Haggai Roitman, Yishay Mansour, Amir Amihood

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

2 Scopus citations

Abstract

We study the problem of item recommendation using complex usage data. We assume that users may interact with items in various ways, each such interaction generates a usage point which may be accompanied with multiple feedback types. In addition, each user may interact with each item multiple times. We propose a generic framework that re-models the user vectors as a post-processing step that can be applied to any Matrix Factorization (MF) method. Using an evaluation on several heterogeneous real-world datasets, we demonstrate the effectiveness of the approach and demonstrate its superiority over two alternative methods.

Original languageEnglish
Title of host publicationICTIR 2017 - Proceedings of the 2017 ACM SIGIR International Conference on the Theory of Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages201-208
Number of pages8
ISBN (Electronic)9781450344906
DOIs
StatePublished - 1 Oct 2017
Event7th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2017 - Amsterdam, Netherlands
Duration: 1 Oct 20174 Oct 2017

Publication series

NameICTIR 2017 - Proceedings of the 2017 ACM SIGIR International Conference on the Theory of Information Retrieval

Conference

Conference7th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2017
Country/TerritoryNetherlands
CityAmsterdam
Period1/10/174/10/17

Bibliographical note

Publisher Copyright:
© 2017 Copyright held by the owner/author(s).

Fingerprint

Dive into the research topics of 'A user re-modeling approach to item recommendation using complex usage data'. Together they form a unique fingerprint.

Cite this