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CLC number: TP391

On-line Access: 2018-01-12

Received: 2016-08-11

Revision Accepted: 2016-11-09

Crosschecked: 2017-11-24

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Jing Wang


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.11 P.1817-1827


A novel confidence estimation method for heterogeneous implicit feedback

Author(s):  Jing Wang, Lan-fen Lin, Heng Zhang, Jia-qi Tu, Peng-hua Yu

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   cswangjing@zju.edu.cn

Key Words:  Recommender systems, Heterogeneous implicit feedback, Confidence, Collaborative filtering, E-commerce

Jing Wang, Lan-fen Lin, Heng Zhang, Jia-qi Tu, Peng-hua Yu. A novel confidence estimation method for heterogeneous implicit feedback[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(11): 1817-1827.

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Implicit feedback, which indirectly reflects opinion through user behaviors, has gained increasing attention in recommender system communities due to its accessibility and richness in real-world applications. A major way of exploiting implicit feedback is to treat the data as an indication of positive and negative preferences associated with vastly varying confidence levels. Such algorithms assume that the numerical value of implicit feedback, such as time of watching, indicates confidence, rather than degree of preference, and a larger value indicates a higher confidence, although this works only when just one type of implicit feedback is available. However, in real-world applications, there are usually various types of implicit feedback, which can be referred to as heterogeneous implicit feedback. Existing methods cannot efficiently infer confidence levels from heterogeneous implicit feedback. In this paper, we propose a novel confidence estimation approach to infer the confidence level of user preference based on heterogeneous implicit feedback. Then we apply the inferred confidence to both point-wise and pair-wise matrix factorization models, and propose a more generic strategy to select effective training samples for pair-wise methods. Experiments on real-world e-commerce datasets from Tmall.com show that our methods outperform the state-of-the-art approaches, considering several commonly used ranking-oriented evaluation criteria.




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