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.
|Title of host publication||Advances in Knowledge Discovery and Data Mining|
|Subtitle of host publication||20th Pacific-Asia Conference, PAKDD 2016|
|Publisher||Springer-Verlag London Ltd.|
|Number of pages||12|
|Publication status||Published - 2016|
|Event||The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016 - The University of Auckland, Auckland, New Zealand|
Duration: 19 Apr 2016 → 22 Apr 2016
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016|
|Period||19/04/16 → 22/04/16|
|Other||The 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.