article

A probabilistic inference model for recommender systems

Authors:

Ning ZhongAuthors Info & Claims

Applied Intelligence, Volume 45, Issue 3

Pages 686 - 694

https://doi.org/10.1007/s10489-016-0783-1

Published: 01 October 2016 Publication History

Abstract

Recommendation is an important application that is employed on the Web. In this paper, we propose a method for recommending items to a user by extending a probabilistic inference model in information retrieval. We regard the user's preference as the query, an item as a document, and explicit and implicit factors as index terms. Additional information sources can be added to the probabilistic inference model, particularly belief networks. The proposed method also uses the belief network model to recommend items by combining expert information. Experimental results on real-world data sets show that the proposed method can improve recommendation effectiveness.

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Cited By

Mandal SMaiti A(2020)Explicit feedback meet with implicit feedback in GPMF: a generalized probabilistic matrix factorization model for recommendationApplied Intelligence10.1007/s10489-020-01643-150:6(1955-1978)Online publication date: 21-Feb-2020
https://dl.acm.org/doi/10.1007/s10489-020-01643-1
Shi HChen LXu ZLyu D(2019)Personalized location recommendation using mobile phone usage informationApplied Intelligence10.1007/s10489-019-01477-649:10(3694-3707)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s10489-019-01477-6
Yin BWei XLiu Y(2019)Finding the most influential product under distribution constraints through dominance testsApplied Intelligence10.1007/s10489-018-1293-049:2(723-740)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s10489-018-1293-0
Show More Cited By

Index Terms

A probabilistic inference model for recommender systems
1. Information systems
  1. Information retrieval
2. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems

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Information & Contributors

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Published In

cover image Applied Intelligence

Applied Intelligence Volume 45, Issue 3

October 2016

385 pages

ISSN:0924-669X

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Copyright © Copyright © 2016 Springer Science+Business Media New York.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 October 2016

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Cited By

Mandal SMaiti A(2020)Explicit feedback meet with implicit feedback in GPMF: a generalized probabilistic matrix factorization model for recommendationApplied Intelligence10.1007/s10489-020-01643-150:6(1955-1978)Online publication date: 21-Feb-2020
https://dl.acm.org/doi/10.1007/s10489-020-01643-1
Shi HChen LXu ZLyu D(2019)Personalized location recommendation using mobile phone usage informationApplied Intelligence10.1007/s10489-019-01477-649:10(3694-3707)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s10489-019-01477-6
Yin BWei XLiu Y(2019)Finding the most influential product under distribution constraints through dominance testsApplied Intelligence10.1007/s10489-018-1293-049:2(723-740)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s10489-018-1293-0
Sahu ADwivedi P(2019)User profile as a bridge in cross-domain recommender systems for sparsity reductionApplied Intelligence10.1007/s10489-018-01402-349:7(2461-2481)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s10489-018-01402-3
Zhang HMin FWu YFu ZGao L(2018)Magic barrier estimation models for recommended systems under normal distributionApplied Intelligence10.1007/s10489-018-1237-848:12(4678-4693)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s10489-018-1237-8

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