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Combinational collaborative filtering for personalized community recommendation

Published: 24 August 2008 Publication History

Abstract

Rapid growth in the amount of data available on social networking sites has made information retrieval increasingly challenging for users. In this paper, we propose a collaborative filtering method, Combinational Collaborative Filtering (CCF), to perform personalized community recommendations by considering multiple types of co-occurrences in social data at the same time. This filtering method fuses semantic and user information, then applies a hybrid training strategy that combines Gibbs sampling and Expectation-Maximization algorithm. To handle the large-scale dataset, parallel computing is used to speed up the model training. Through an empirical study on the Orkut dataset, we show CCF to be both effective and scalable.

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    cover image ACM Conferences
    KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
    August 2008
    1116 pages
    ISBN:9781605581934
    DOI:10.1145/1401890
    • General Chair:
    • Ying Li,
    • Program Chairs:
    • Bing Liu,
    • Sunita Sarawagi
    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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    Published: 24 August 2008

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    Author Tags

    1. collaborative filtering
    2. personalized recommendation
    3. probabilistic models

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    KDD '08 Paper Acceptance Rate 118 of 593 submissions, 20%;
    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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    • (2023)Ranking-based Group Identification via Factorized Attention on Social Tripartite GraphProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570406(769-777)Online publication date: 27-Feb-2023
    • (2023)Research on knn algorithm based on kmeans clustering and collaborative filtering hybrid algorithm in AI teaching2023 8th International Conference on Information Systems Engineering (ICISE)10.1109/ICISE60366.2023.00102(453-456)Online publication date: 23-Jun-2023
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    • (2020)Leveraging personality information to improve community recommendation in e‐learning platformsBritish Journal of Educational Technology10.1111/bjet.1301151:5(1711-1733)Online publication date: 6-Aug-2020
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