Exploiting user feedbacks in matrix factorization for recommender systems

Haiyang Zhang*, Nikola S. Nikolov, Ivan Ganchev

*Corresponding author for this work

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

7 Citations (Scopus)


With the rapid growth of the Web, recommender systems have become essential tools to assist users to find high-quality personalized recommendations from massive information resources. Content-based filtering (CB) and collaborative filtering (CF) are the two most popular and widely used recommendation approaches. In this paper, we focus on ways of taking advantage of both approaches based only on user-item rating data. Motivated by the user profiling technique used in content-based recommendation, we propose to merge user profiles, learnt from the items viewed by the users, as a new latent variable in the latent factor model, which is one of the most popular CF-based approaches, thereby generating more accurate recommendation models. The performance of the proposed models is tested against several widely-deployed state-of-the-art recommendation methods. Experimental results, based on two popular datasets, confirm that better accuracy can be indeed achieved.

Original languageEnglish
Title of host publicationModel and Data Engineering - 7th International Conference, MEDI 2017, Proceedings
EditorsAlberto Abello, Yassine Ouhammou, Ladjel Bellatreche, Mirjana Ivanovic
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319668536
Publication statusPublished - 2017
Externally publishedYes
Event7th International Conference on Model and Data Engineering, MEDI 2017 - Barcelona, Spain
Duration: 4 Oct 20176 Oct 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10563 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Model and Data Engineering, MEDI 2017


  • Collaborative filtering
  • Matrix factorization
  • Recommender systems
  • User feedback

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