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Journal of Intelligent Information Systems

Erschienen in:

01.06.2015

A hybrid approach of topic model and matrix factorization based on two-step recommendation framework

verfasst von: Xiangyu Zhao, Zhendong Niu, Wei Chen, Chongyang Shi, Ke Niu, Donglei Liu

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 3/2015

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Abstract

Recommender systems become increasingly significant in solving the information explosion problem. Two typical kinds of techniques treat the recommendation problem as either a rating prediction or a ranking prediction one. In contrast, we propose a two-step framework that considers recommendation as a simulation of users’ behaviors to generate ratings. The first step is to predict the probability that a user rates an item, and the second step is to predict rating values. After that, the predicted results from both steps are combined to compute the expectations of users’ ratings on items, which are used to generate recommendations. Based on this framework, we propose a hybrid approach which uses topic model in the first step and matrix factorization in the second to solve the recommendation problem. Experiments with MovieLens and EachMovie datasets demonstrate the effectiveness of the proposed framework and the recommendation approach.

Vorheriger Artikel Effectively and efficiently supporting roll-up and drill-down OLAP operations over continuous dimensions via hierarchical clustering

Nächster Artikel Advanced management of data integrity: property-based testing for business rules

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0 is a typical value out of the range of rating scale, which can be used to distinguish the rating value and the rating behavior.

The effectiveness is demonstrated in the experiment section, in which HTMMF using rating behaviors in the first step gets better results than the one (L&S) with same model but using rating values in the first step.

Types of implicit feedback include rating behaviors, purchase history, browsing history, and search patterns.

http://www.grouplens.org/node/73

http://www.research.digital.com/src/eachmovie

The original ratings from EM1 are in 0-to-1 scale. We convert it to 0-to-5 scale, and then exclude the ratings with value 0 in order to fit the proposed recommendation framework.

Since the restriction of the column width, we only report some typical performances for different K _T.

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Titel: A hybrid approach of topic model and matrix factorization based on two-step recommendation framework
verfasst von: Xiangyu Zhao
Zhendong Niu
Wei Chen
Chongyang Shi
Ke Niu
Donglei Liu
Publikationsdatum: 01.06.2015
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 3/2015
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-014-0334-3

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