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Attribute-Based Influence Maximization in Social Networks Read chapter Read first chapter

2016 | OriginalPaper | Chapter

A Personalized Recommendation Algorithm for User-Preference Similarity Through the Semantic Analysis

Authors : Haolin Zhang, Feiyue Ye

Published in: Web Information Systems Engineering – WISE 2016

Publisher: Springer International Publishing

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Abstract

Traditional personalized recommendation algorithms do not involve the analysis of semantic information, so the recommendation results are less accurate. Aiming at this problem, based on the semantic analysis, genres similarity and content features similarity of the projects rated by users are used to measure user-preference and therefore calculate users similarity. Moreover, the number of projects in the same genres is applied to measure project-relevancy and thereby project similarity. Based on these, this study puts forward a personalized recommendation algorithm for user-preference similarity through the semantic analysis. The contrast experiment results based on Movielens data set show that the recommendation accuracy and quality of the proposed algorithm are significantly improved.

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previous chapter Taxonomy Tree Based Similarity Measurement of Textual Attributes of Items for Recommender Systems
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Metadata
Title
A Personalized Recommendation Algorithm for User-Preference Similarity Through the Semantic Analysis
Authors
Haolin Zhang
Feiyue Ye
Copyright Year
2016
Book
Web Information Systems Engineering – WISE 2016

Print ISBN: 978-3-319-48739-7

Electronic ISBN: 978-3-319-48740-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-48740-3_22

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