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Erschienen in:
Database Performance Tuning and Query Optimization Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Optimization Matrix Factorization Recommendation Algorithm Based on Rating Centrality

verfasst von : Zhipeng Wu, Hui Tian, Xuzhen Zhu, Shuo Wang

Erschienen in: Data Mining and Big Data

Verlag: Springer International Publishing

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Abstract

Matrix factorization (MF) is extensively used to mine the user preference from explicit ratings in recommender systems. However, the reliability of explicit ratings is not always consistent, because many factors may affect the user’s final evaluation on an item, including commercial advertising and a friend’s recommendation. Therefore, mining the reliable ratings of user is critical to further improve the performance of the recommender system. In this work, we analyze the deviation degree of each rating in overall rating distribution of user and item, and propose the notion of user-based rating centrality and item-based rating centrality, respectively. Moreover, based on the rating centrality, we measure the reliability of each user rating and provide an optimized matrix factorization recommendation algorithm. Experimental results on two popular recommendation datasets reveal that our method gets better performance compared with other matrix factorization recommendation algorithms, especially on sparse datasets.

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Vorheriges Kapitel Explainable Matrix Factorization with Constraints on Neighborhood in the Latent Space
Nächstes Kapitel An Entropy-Based Similarity Measure for Collaborative Filtering
Fußnoten
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Metadaten
Titel
Optimization Matrix Factorization Recommendation Algorithm Based on Rating Centrality
verfasst von
Zhipeng Wu
Hui Tian
Xuzhen Zhu
Shuo Wang
Copyright-Jahr
2018
Buch
Data Mining and Big Data

Print ISBN: 978-3-319-93802-8

Electronic ISBN: 978-3-319-93803-5

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-93803-5_11