Coupled item-based matrix factorization

Fangfang LI, Guandong XU, Longbing CAO

Research output: Chapter in Book/Report/Conference proceedingChapters

10 Citations (Scopus)

Abstract

The essence of the challenges cold start and sparsity in Recommender Systems (RS) is that the extant techniques, such as Collaborative Filtering (CF) and Matrix Factorization (MF), mainly rely on the user-item rating matrix, which sometimes is not informative enough for predicting recommendations. To solve these challenges, the objective item attributes are incorporated as complementary information. However, most of the existing methods for inferring the relationships between items assume that the attributes are “independently and identically distributed (iid)”, which does not always hold in reality. In fact, the attributes are more or less coupled with each other by some implicit relationships. Therefore, in this paper we propose an attribute-based coupled similarity measure to capture the implicit relationships between items. We then integrate the implicit item coupling into MF to form the Coupled Item-based Matrix Factorization (CIMF) model. Experimental results on two open data sets demonstrate that CIMF outperforms the benchmark methods. Copyright © 2014 Springer International Publishing Switzerland.

Original languageEnglish
Title of host publicationWeb information systems engineering -- WISE 2014: 15th International Conference, Thessaloniki, Greece, October 12-14, 2014, proceedings, part I
EditorsBoualem BENATALLAH, Azer BESTAVROS, Yannis MANOLOPOULOS, Athena VAKALI, Yanchun ZHANG
Place of PublicationCham
PublisherSpringer
Pages1-14
ISBN (Electronic)9783319117492
ISBN (Print)9783319117485
DOIs
Publication statusPublished - 2014

Citation

Li, F., Xu, G., & Cao, L. (2014). Coupled item-based matrix factorization. In B. Benatallah, A. Bestavros, Y. Manolopoulos, A. Vakali, & Y. Zhang (Eds.), Web information systems engineering -- WISE 2014: 15th International Conference, Thessaloniki, Greece, October 12-14, 2014, proceedings, part I (pp. 1-14). Springer. https://doi.org/10.1007/978-3-319-11749-2_1

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