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Database Performance Tuning and Query Optimization Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Three-Segment Similarity Measure Model for Collaborative Filtering

Author : Fangyi Hu

Published in: Data Mining and Big Data

Publisher: Springer International Publishing

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Abstract

Collaborative filtering is one of the most widely used method in personalized recommendations. The most important step of the method is obtaining the similarities among users by using ratings information so that system can predict user preferences. However, most similarity measures are not efficient enough in the face of cold start and data sparsity problem. To measure user similarity comprehensively and objectively, this paper introduces a segmented similarity measure model. This model not only calculates the similarity model based on the number of user ratings but also makes full use of user attribute similarity and item similarity to improve the accuracy of similarity. Experiments using two reals datasets show that the proposed method relieves cold start and data sparsity issues and improves the prediction accuracy and recommendation quality.

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previous chapter An Entropy-Based Similarity Measure for Collaborative Filtering
next chapter Factor Analysis of the Psychosocial Risk Assessment Instrument
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Metadata
Title
Three-Segment Similarity Measure Model for Collaborative Filtering
Author
Fangyi Hu
Copyright Year
2018
Book
Data Mining and Big Data

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

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

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-93803-5_13

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