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FISM: factored item similarity models for top-N recommender systems

Published: 11 August 2013 Publication History

Abstract

The effectiveness of existing top-N recommendation methods decreases as the sparsity of the datasets increases. To alleviate this problem, we present an item-based method for generating top-N recommendations that learns the item-item similarity matrix as the product of two low dimensional latent factor matrices. These matrices are learned using a structural equation modeling approach, wherein the value being estimated is not used for its own estimation. A comprehensive set of experiments on multiple datasets at three different sparsity levels indicate that the proposed methods can handle sparse datasets effectively and outperforms other state-of-the-art top-N recommendation methods. The experimental results also show that the relative performance gains compared to competing methods increase as the data gets sparser.

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  • (2024)Enhancing Knowledge-Concept Recommendations with Heterogeneous Graph-Contrastive LearningMathematics10.3390/math1215232412:15(2324)Online publication date: 25-Jul-2024
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  • (2024)Disentangled Self-Attention with Auto-Regressive Contrastive Learning for Neural Group RecommendationApplied Sciences10.3390/app1410415514:10(4155)Online publication date: 14-May-2024
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    cover image ACM Conferences
    KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
    August 2013
    1534 pages
    ISBN:9781450321747
    DOI:10.1145/2487575
    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: 11 August 2013

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    Author Tags

    1. item similarity
    2. recommender systems
    3. sparse data
    4. topn

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    KDD '13 Paper Acceptance Rate 125 of 726 submissions, 17%;
    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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    Cited By

    View all
    • (2024)Enhancing Knowledge-Concept Recommendations with Heterogeneous Graph-Contrastive LearningMathematics10.3390/math1215232412:15(2324)Online publication date: 25-Jul-2024
    • (2024)Improving Graph Collaborative Filtering from the Perspective of User–Item Interaction Directly Using Contrastive LearningMathematics10.3390/math1213205712:13(2057)Online publication date: 30-Jun-2024
    • (2024)Disentangled Self-Attention with Auto-Regressive Contrastive Learning for Neural Group RecommendationApplied Sciences10.3390/app1410415514:10(4155)Online publication date: 14-May-2024
    • (2024)Simplify to the Limit! Embedding-Less Graph Collaborative Filtering for Recommender SystemsACM Transactions on Information Systems10.1145/370123043:1(1-30)Online publication date: 19-Oct-2024
    • (2024)Discrete Federated Multi-behavior Recommendation for Privacy-Preserving Heterogeneous One-Class Collaborative FilteringACM Transactions on Information Systems10.1145/365285342:5(1-50)Online publication date: 29-Apr-2024
    • (2024)Neighborhood-Based Collaborative Filtering for Conversational RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688191(1045-1050)Online publication date: 8-Oct-2024
    • (2024)MMGCL: Meta Knowledge-Enhanced Multi-view Graph Contrastive Learning for RecommendationsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688127(538-548)Online publication date: 8-Oct-2024
    • (2024)Discerning Canonical User Representation for Cross-Domain RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688114(318-328)Online publication date: 8-Oct-2024
    • (2024)PACIFIC: Enhancing Sequential Recommendation via Preference-aware Causal Intervention and Counterfactual Data AugmentationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679803(249-258)Online publication date: 21-Oct-2024
    • (2024)OpenSiteRec: An Open Dataset for Site RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657875(1483-1493)Online publication date: 10-Jul-2024
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