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A probabilistic rating auto-encoder for personalized recommender systems

Liang, H. and Baldwin, T. (2015) A probabilistic rating auto-encoder for personalized recommender systems. In: Conference on Information and Knowledge Management, 19-23 October, Melbourne, Australia.

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Official URL: https://dl.acm.org/citation.cfm?doid=2806416.28066...

Abstract/Summary

User profiling is a key component of personalized recommender systems, and is used to generate user profiles that describe individual user interests and preferences. The increasing availability of big data is driving the urgent need for user profiling algorithms that are able to generate accurate user profiles from large-scale user behavior data. In this paper, we propose a probabilistic rating auto-encoder to perform unsupervised feature learning and generate latent user feature profiles from large-scale user rating data. Based on the generated user profiles, neighborhood based collaborative filtering approaches have been adopted to make personalized rating predictions. The effectiveness of the proposed approach is demonstrated in experiments conducted on a real-world rating dataset from yelp.com.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:81990

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