Applying HOSVD to Alleviate the Sparsity Problem in Context-aware Recommender Systems

It is quite a great challenge for Context- aware recommender systems （CARS） to generate accurate recommendations with only a few available none-zero con= textual user preferences. This paper presents a new ap- proach to alleviate this high sparslty problem by applying the Higher order singular value...

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Bibliographic Details
Published in:电子学报：英文版 2013-10, Vol.22 (4), p.773-778
Main Author: WANG Licai MENG Xiangwu ZHANG Yujie
Format: Article
Language:English
Subjects:
上下文
奇异值分解
应用
感知
推荐系统
用户偏好
稀疏
线性化方法
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Summary:It is quite a great challenge for Context- aware recommender systems （CARS） to generate accurate recommendations with only a few available none-zero con= textual user preferences. This paper presents a new ap- proach to alleviate this high sparslty problem by applying the Higher order singular value decomposition （HOSVD） technique. Firstly, it constructs an N-order tensor to rep- resent multidimensional contextual user preferences and decompose it into （N- 2） 3-order tensors according to different types of context （such as time, location and ac- tivity）. Secondly, it introduces HOSVD to automatically discover the latent associations among these different 3- dimensional objects and predicts unknown unidimensional contextual user preferences. Finally, it calculates every contextual influence coefficient that each type of context factor influences user preferences and then constructs a new N-order tensor using weighted linearization method in order to provide recommendations. Experimental eval- uation on a simulated personalized mobile services envi- ronment demonstrates the efficacy of our approach against the other baseline ones.
ISSN:1022-4653