research-article

A Novel Friend Recommendation Algorithm Based on Intimacy and LDA Model

Authors:

Wang Zhen-wu,

Han Xiao-hui,

Tian Hao-mingAuthors Info & Claims

ICIME 2017: Proceedings of the 9th International Conference on Information Management and Engineering

Pages 128 - 132

https://doi.org/10.1145/3149572.3149591

Published: 09 October 2017 Publication History

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Abstract

With the development of Internet, various social network service (SNS) platforms appeared, such as Facebook, twitter, Flickr, Sina microblog, and so on. Friend recommendation is the key issue for the SNS which can enhance the interactivity among SNS users.A novel recommendation algorithm is proposed in this paper, it applies time line to compute the interactions among target user and his/her recommended friends firstly, which predicts the intimacy trend and fits intimacy with interactive information at different time slots; then a Latent Dirichlet Allocation (LDA) model is used to generate subjects and judge the subject similarities between target user and recommended friends, at last, the two parts have been combined by an information entropy method which adjust the weight information dynastically during the friend recommendation process. Compared with collaborative filtering recommendation algorithm and LDA method, the experimental results proved that the proposed algorithm has got better performance.

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Cited By

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Xin MWu L(2022)Using multi-features to partition users for friends recommendation in location based social networkInformation Processing and Management: an International Journal10.1016/j.ipm.2019.10212557:1Online publication date: 21-Apr-2022
https://dl.acm.org/doi/10.1016/j.ipm.2019.102125

Index Terms

A Novel Friend Recommendation Algorithm Based on Intimacy and LDA Model
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social recommendation

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Published In

ICIME 2017: Proceedings of the 9th International Conference on Information Management and Engineering

October 2017

233 pages

ISBN:9781450353373

DOI:10.1145/3149572

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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University of Salford: University of Salford

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 October 2017

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ICIME 2017

ICIME 2017: 2017 9th International Conference on Information Management and Engineering

October 9 - 11, 2017

Barcelona, Spain

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Overall Acceptance Rate 19 of 31 submissions, 61%

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Xin MWu L(2022)Using multi-features to partition users for friends recommendation in location based social networkInformation Processing and Management: an International Journal10.1016/j.ipm.2019.10212557:1Online publication date: 21-Apr-2022
https://dl.acm.org/doi/10.1016/j.ipm.2019.102125

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Recommendations

Social Friend Recommendation Based on Network Correlation and Feature Co-Clustering

Informational friend recommendation in social media

Friend Recommendation for Location-Based Mobile Social Networks