research-article

Collaborative filtering algorithm based on rating distance

Authors:

Jiamin LuAuthors Info & Claims

IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication

Article No.: 66, Pages 1 - 7

https://doi.org/10.1145/3022227.3022292

Published: 05 January 2017 Publication History

Abstract

Collaborative filtering, a successful and wildly used technique in personalized recommender systems, generates recommendations by similar users. Cosine similarity and Pearson correlation coefficient are widely used in collaborative filtering to calculate the similarity; however, the similarity is not accurate in some cases because of the defects of the algorithm. To solve these issues, this paper proposes a novel similarity calculation method which combined information entropy with compressive distance weight based on the probability distribution of rating distance. Experiment results show that the proposed method get better performance than conventional Pearson correlation coefficient method.

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Cited By

Singh RSingh PSingh JSingh ADhanasekaran S(2022)Utilizing Alike Neighbor Influenced Similarity Metric for Efficient Prediction in Collaborative Filter-Approach-Based Recommendation SystemApplied Sciences10.3390/app12221168612:22(11686)Online publication date: 17-Nov-2022
https://doi.org/10.3390/app122211686
Singh PSinha SChoudhury P(2022)An improved item-based collaborative filtering using a modified Bhattacharyya coefficient and user–user similarity as weightKnowledge and Information Systems10.1007/s10115-021-01651-8Online publication date: 25-Jan-2022
https://doi.org/10.1007/s10115-021-01651-8
Munaji AEmanuel A(2021)Restaurant Recommendation System Based on User Ratings with Collaborative FilteringIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/1077/1/0120261077:1(012026)Online publication date: 1-Feb-2021
https://doi.org/10.1088/1757-899X/1077/1/012026
Show More Cited By

Index Terms

Collaborative filtering algorithm based on rating distance
1. Information systems

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Published In

cover image ACM Conferences

IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication

January 2017

746 pages

ISBN:9781450348881

DOI:10.1145/3022227

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 January 2017

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National Science and Technology Support Plan Project of China
National Natural Science Foundation of China

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IMCOM '17

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SIGAPP

IMCOM '17: The 11th International Conference on Ubiquitous Information Management and Communication

January 5 - 7, 2017

Beppu, Japan

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IMCOM '17 Paper Acceptance Rate 113 of 366 submissions, 31%;

Overall Acceptance Rate 213 of 621 submissions, 34%

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Cited By

Singh RSingh PSingh JSingh ADhanasekaran S(2022)Utilizing Alike Neighbor Influenced Similarity Metric for Efficient Prediction in Collaborative Filter-Approach-Based Recommendation SystemApplied Sciences10.3390/app12221168612:22(11686)Online publication date: 17-Nov-2022
https://doi.org/10.3390/app122211686
Singh PSinha SChoudhury P(2022)An improved item-based collaborative filtering using a modified Bhattacharyya coefficient and user–user similarity as weightKnowledge and Information Systems10.1007/s10115-021-01651-8Online publication date: 25-Jan-2022
https://doi.org/10.1007/s10115-021-01651-8
Munaji AEmanuel A(2021)Restaurant Recommendation System Based on User Ratings with Collaborative FilteringIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/1077/1/0120261077:1(012026)Online publication date: 1-Feb-2021
https://doi.org/10.1088/1757-899X/1077/1/012026
Singh PAhmed RRajput IChoudhury P(2021)A Comparative Study on Prediction Approaches of Item-Based Collaborative Filtering in Neighborhood-Based RecommendationsWireless Personal Communications10.1007/s11277-021-08662-2Online publication date: 27-Jun-2021
https://doi.org/10.1007/s11277-021-08662-2
Maruf ZLaksito A(2020)The Comparison of Distance Measurement for Optimizing KNN Collaborative Filtering Recommender System2020 3rd International Conference on Information and Communications Technology (ICOIACT)10.1109/ICOIACT50329.2020.9332108(89-93)Online publication date: 24-Nov-2020
https://doi.org/10.1109/ICOIACT50329.2020.9332108
Singh PSinha MDas SChoudhury P(2020)Enhancing recommendation accuracy of item-based collaborative filtering using Bhattacharyya coefficient and most similar itemApplied Intelligence10.1007/s10489-020-01775-4Online publication date: 2-Aug-2020
https://doi.org/10.1007/s10489-020-01775-4
Cai ZWang JLi YLiu S(2017)A Collaborative Filtering Recommendation Algorithm Based on the Difference and the Correlation of Users’ RatingsData Science10.1007/978-981-10-6385-5_5(52-63)Online publication date: 16-Sep-2017
https://doi.org/10.1007/978-981-10-6385-5_5

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