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Support vector machines for collaborative filtering

Published: 10 March 2006 Publication History

Abstract

Support Vector Machines (SVMs) have successfully shown efficiencies in many areas such as text categorization. Although recommendation systems share many similarities with text categorization, the performance of SVMs in recommendation systems is not acceptable due to the sparsity of the user-item matrix. In this paper, we propose a heuristic method to improve the predictive accuracy of SVMs by repeatedly correcting the missing values in the user-item matrix. The performance comparison to other algorithms has been conducted. The experimental studies show that the accurate rates of our heuristic method are the highest.

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    cover image ACM Other conferences
    ACMSE '06: Proceedings of the 44th annual ACM Southeast Conference
    March 2006
    823 pages
    ISBN:1595933158
    DOI:10.1145/1185448
    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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    Publication History

    Published: 10 March 2006

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    Author Tags

    1. collaborative filtering
    2. machine learning
    3. recommendation systems
    4. support vector machines

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    ACM SE06
    ACM SE06: ACM Southeast Regional Conference
    March 10 - 12, 2006
    Florida, Melbourne

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    ACMSE '06 Paper Acceptance Rate 100 of 244 submissions, 41%;
    Overall Acceptance Rate 502 of 1,023 submissions, 49%

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    • (2022)Performance Comparison of Randomized and Non-Randomized Learning Algorithms based Recommender SystemsInternational Journal of Next-Generation Computing10.47164/ijngc.v13i3.820Online publication date: 31-Oct-2022
    • (2022)A Multi-Criteria Collaborative Filtering Approach Using Deep Learning and Dempster-Shafer Theory for Hotel RecommendationsIEEE Access10.1109/ACCESS.2022.316531010(37281-37293)Online publication date: 2022
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