Local item-item models for top-N recommendation

Evangelia Christakopoulou, George Karypis

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

54 Scopus citations

Abstract

Item-based approaches based on SLIM (Sparse LInear Methods) have demonstrated very good performance for top-N recommendation; however they only estimate a single model for all the users. This work is based on the intuition that not all users behave in the same way - instead there exist subsets of like-minded users. By using different item-item models for these user subsets, we can capture differences in their preferences and this can lead to improved performance for top-N recommendations. In this work, we extend SLIM by combining global and local SLIM models. We present a method that computes the prediction scores as a user-specific combination of the predictions derived by a global and local itemitem models. We present an approach in which the global model, the local models, their user-specific combination, and the assignment of users to the local models are jointly optimized to improve the top-N recommendation performance. Our experiments show that the proposed method improves upon the standard SLIM model and outperforms competing top-N recommendation approaches.

Original languageEnglish (US)
Title of host publicationRecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages67-74
Number of pages8
ISBN (Electronic)9781450340359
DOIs
StatePublished - Sep 7 2016
Event10th ACM Conference on Recommender Systems, RecSys 2016 - Boston, United States
Duration: Sep 15 2016Sep 19 2016

Publication series

NameRecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems

Other

Other10th ACM Conference on Recommender Systems, RecSys 2016
Country/TerritoryUnited States
CityBoston
Period9/15/169/19/16

Bibliographical note

Funding Information:
This work was supported in part by NSF (OCI-1048018, IIS-1247632, IIP-1414153, IIS-1447788), Army Research Office (W911NF-14-1-0316), Intel Software and Services Group, and the Digital Technology Center at the University of Minnesota. Access to research and computing facilities was provided by the Digital Technology Center and the Minnesota Supercomputing Institute.

Publisher Copyright:
© 2016 ACM.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

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