Enhanced SVD for collaborative filtering

Xin Guan, Chang Tsun Li, Yu Guan

Research output: Book chapter/Published conference paperConference paperpeer-review

6 Citations (Scopus)


Matrix factorization is one of the most popular techniques for prediction problems in the fields of intelligent systems and data mining. It has shown its effectiveness in many real-world applications such as recommender systems. As a collaborative filtering method, it gives users recommendations based on their previous preferences (or ratings). Due to the extreme sparseness of the ratings matrix, active learning is used for eliciting ratings for a user to get better recommendations. In this paper, we propose a new matrix factorization model called Enhanced SVD (ESVD) which combines the classic matrix factorization method with a specific rating elicitation strategy. We evaluate the proposed ESVD method on the Movielens data set, and the experimental results suggest its effectiveness in terms of both accuracy and efficiency, when compared with traditional matrix factorization methods and active learning methods.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication20th Pacific-Asia Conference, PAKDD 2016
PublisherSpringer-Verlag London Ltd.
Number of pages12
Volume9652 LNAI
ISBN (Print)9783319317496
Publication statusPublished - 2016
EventThe 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016 - The University of Auckland, Auckland, New Zealand
Duration: 19 Apr 201622 Apr 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9652 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceThe 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016
CountryNew Zealand
OtherThe Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)
is a leading international conference in the areas of knowledge discovery and data mining (KDD). It provides an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems and the emerging applications.
Internet address

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