Feature-based factorized Bilinear Similarity Model for cold-start Top-n item recommendation

Mohit Sharma, Jiayu Zhou, Junling Hu, George Karypis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

Recommending new items to existing users has remained a challenging problem due to absence of user's past preferences for these items. The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem. These methods rely on similarities between the target item and user's previous preferred items. While computing similarities based on item features, these methods overlook the interactions among the features of the items and consider them independently. Modeling interactions among features can be helpful as some features, when considered together, provide a stronger signal on the relevance of an item when compared to case where features are considered independently. To address this important issue, in this work we introduce the Feature-based factorized Bilinear Similarity Model (FBSM), which learns factorized bilinear similarity model for Top-n recommendation of new items, given the information about items preferred by users in past as well as the features of these items. We carry out extensive empirical evaluations on benchmark datasets, and we find that the proposed FBSM approach improves upon traditional non-collaborative methods in terms of recommendation performance. Moreover, the proposed approach also learns insightful interactions among item features from data, which lead to deep understanding on how these interactions contribute to personalized recommendation.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2015, SDM 2015
EditorsSuresh Venkatasubramanian, Jieping Ye
PublisherSociety for Industrial and Applied Mathematics Publications
Pages190-198
Number of pages9
ISBN (Electronic)9781510811522
DOIs
StatePublished - 2015
EventSIAM International Conference on Data Mining 2015, SDM 2015 - Vancouver, Canada
Duration: Apr 30 2015May 2 2015

Publication series

NameSIAM International Conference on Data Mining 2015, SDM 2015

Other

OtherSIAM International Conference on Data Mining 2015, SDM 2015
Country/TerritoryCanada
CityVancouver
Period4/30/155/2/15

Bibliographical note

Funding Information:
This work was supported in part by NSF (IIS-0905220, OCI-1048018, CNS-1162405, IIS-1247632, IIP-1414153, IIS-1447788), Army Research Office (W911XF-14-1-0316), Samsung Research America, and the Digital Technology Center at the University of Minnesota.

Publisher Copyright:
Copyright © SIAM.

Fingerprint

Dive into the research topics of 'Feature-based factorized Bilinear Similarity Model for cold-start Top-n item recommendation'. Together they form a unique fingerprint.

Cite this