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VSRank: A Novel Framework for Ranking-Based Collaborative Filtering

Published: 17 July 2014 Publication History

Abstract

Collaborative filtering (CF) is an effective technique addressing the information overload problem. CF approaches generally fall into two categories: rating based and ranking based. The former makes recommendations based on historical rating scores of items and the latter based on their rankings. Ranking-based CF has demonstrated advantages in recommendation accuracy, being able to capture the preference similarity between users even if their rating scores differ significantly. In this study, we propose VSRank, a novel framework that seeks accuracy improvement of ranking-based CF through adaptation of the vector space model. In VSRank, we consider each user as a document and his or her pairwise relative preferences as terms. We then use a novel degree-specialty weighting scheme resembling TF-IDF to weight the terms. Extensive experiments on benchmarks in comparison with the state-of-the-art approaches demonstrate the promise of our approach.

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    cover image ACM Transactions on Intelligent Systems and Technology
    ACM Transactions on Intelligent Systems and Technology  Volume 5, Issue 3
    Special Section on Urban Computing
    September 2014
    361 pages
    ISSN:2157-6904
    EISSN:2157-6912
    DOI:10.1145/2648782
    • Editor:
    • Qiang Yang
    Issue’s Table of Contents
    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: 17 July 2014
    Accepted: 01 October 2013
    Revised: 01 September 2013
    Received: 01 May 2013
    Published in TIST Volume 5, Issue 3

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    Author Tags

    1. Recommender systems
    2. collaborative filtering
    3. ranking-based collaborative filtering
    4. term weighting
    5. vector space model

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    • (2022)A Matrix Factorization Model for Hellinger-Based Trust Management in Social Internet of ThingsIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2021.305295319:4(2274-2285)Online publication date: 1-Jul-2022
    • (2022)Beyond pairwise comparisons in social choiceTheoretical Computer Science10.1016/j.tcs.2021.07.004904:C(27-47)Online publication date: 15-Feb-2022
    • (2021)Holistic Transfer to Rank for Top-N RecommendationACM Transactions on Interactive Intelligent Systems10.1145/343436011:1(1-1)Online publication date: 15-Mar-2021
    • (2021)POI Recommend for Deep Neural Network Based on Explicit and Implicit Feature Joint2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI52525.2021.00057(352-358)Online publication date: Nov-2021
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    • (2020)Transfer to Rank for Top-N RecommendationIEEE Transactions on Big Data10.1109/TBDATA.2019.28924786:4(770-779)Online publication date: 1-Dec-2020
    • (2019)Listwise Collaborative Filtering with High-Rating-Based Similarity and Simple Missing Value EstimationJournal of Japan Society for Fuzzy Theory and Intelligent Informatics10.3156/jsoft.31.1_50131:1(501-507)Online publication date: 15-Feb-2019
    • (2019)Personalized recommendation via user preference matchingInformation Processing & Management10.1016/j.ipm.2019.02.00256:3(955-968)Online publication date: May-2019
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