Exploiting implicit item relationships for recommender systems

Zhu Sun*, Guibing Guo, Jie Zhang

*Corresponding author for this work

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

10 Citations (Scopus)


Collaborative filtering inherently suffers from the data sparsity and cold start problems. Social networks have been shown useful to help alleviate these issues. However, social connections may not be available in many real systems, whereas implicit item relationships are lack of study. In this paper, we propose a novel matrix factorization model by taking into account implicit item relationships. Specifically, we employ an adapted association rule technique to reveal implicit item relationships in terms of item-to-item and group-to-item associations, which are then used to regularize the generation of low-rank user- and item-feature matrices. Experimental results on four real-world datasets demonstrate the superiority of our proposed approach against other counterparts.

Original languageEnglish
Title of host publicationUser Modeling, Adaptation and Personalization
Subtitle of host publication23rd International Conference, UMAP 2015, Proceedings
EditorsFrancesco Ricci, Kalina Bontcheva, Owen Conlan, Séamus Lawless
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Number of pages13
ISBN (Electronic)9783319202679
ISBN (Print)9783319202662
Publication statusPublished - 2015
Externally publishedYes
Event23rd International Conference on User Modeling, Adaptation and Personalization, UMAP 2015 - Dublin, Ireland
Duration: 29 Jun 20153 Jul 2015

Publication series

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


Conference23rd International Conference on User Modeling, Adaptation and Personalization, UMAP 2015

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