Cross domain recommendation using multidimensional tensor factorization

Anu Taneja

doi:10.1016/j.eswa.2017.09.042

2018-02-01

PRE

Anu Taneja *

Anuja Arora

DOI：10.1016/j.eswa.2017.09.042

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摘要

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In the era of social media, exponential growth of information generated by online social media and e-commerce applications demands expert and intelligent recommendation systems. It has become one of the most valuable tools as it reduces problems such as information overload while selecting and suggesting friends, items, products, jobs etc according to users' interests. Recommendation uses Collaborative Filtering as one of the most popular approaches but the major limitations of this approach are sparsity and cold-start issues. Mostly existing recommendation systems focus on a single domain, on the other end cross-domain collaborative filtering is able to alleviate the degree of sparsity and cold-start problems to a better extent. To avoid these problems, cross domain evolution comes in limelight and has become an emerging topic of research nowadays. This paper mainly discusses the notion of cross-domain recommendation, its techniques and proposes a generalized Cross Domain-Multi Dimension Tensor Factorization (CD-MDTF) approach to trade off influence among domains optimally. Cross Domain recommendation system employs knowledge from source domain and commingles it to target domain which covers the aspect of intelligent behavior and brings it to the category of an expert system. Finally, to evaluate the proposed CD-MDTF approach, experiments are performed on two real-world datasets, Movie-Lens and Book-Crossing. Results validate that sparsity and cold start problem, is reduced by 16% and 25% respectively in comparison to single-domain recommendation systems. Further, the proposed CD-MDTF recommendation system accuracy is validated using precision and recall as evaluation performance metrics which shows an improvement of 41% in precision and 21% in recall. The results show that embedding of multiple domains and multiple dimensions for recommendation helps in result improvement, thereby augmenting the recommendation system performance like an expert and intelligent system. (C) 2017 Elsevier Ltd. All rights reserved.

Keyword:

Collaborative filtering

Cross domain recommendation systems

Tensor factorization

Clustering

Cross-domain multi-dimension tensor factorization