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Convolutional Matrix Factorization for Document Context-Aware Recommendation

Published: 07 September 2016 Publication History

Abstract

Sparseness of user-to-item rating data is one of the major factors that deteriorate the quality of recommender system. To handle the sparsity problem, several recommendation techniques have been proposed that additionally consider auxiliary information to improve rating prediction accuracy. In particular, when rating data is sparse, document modeling-based approaches have improved the accuracy by additionally utilizing textual data such as reviews, abstracts, or synopses. However, due to the inherent limitation of the bag-of-words model, they have difficulties in effectively utilizing contextual information of the documents, which leads to shallow understanding of the documents. This paper proposes a novel context-aware recommendation model, convolutional matrix factorization (ConvMF) that integrates convolutional neural network (CNN) into probabilistic matrix factorization (PMF). Consequently, ConvMF captures contextual information of documents and further enhances the rating prediction accuracy. Our extensive evaluations on three real-world datasets show that ConvMF significantly outperforms the state-of-the-art recommendation models even when the rating data is extremely sparse. We also demonstrate that ConvMF successfully captures subtle contextual difference of a word in a document. Our implementation and datasets are available at http://dm.postech.ac.kr/ConvMF.

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cover image ACM Conferences
RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems
September 2016
490 pages
ISBN:9781450340359
DOI:10.1145/2959100
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: 07 September 2016

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Author Tags

  1. collaborative filtering
  2. contexual information
  3. deep learning
  4. document modeling
  5. neural network' context-aware recommendation
  6. recommender system

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RecSys '16
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RecSys '16: Tenth ACM Conference on Recommender Systems
September 15 - 19, 2016
Massachusetts, Boston, USA

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RecSys '16 Paper Acceptance Rate 29 of 159 submissions, 18%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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  • (2025)ConvSeq-MFNeurocomputing10.1016/j.neucom.2024.128932618:COnline publication date: 14-Feb-2025
  • (2025)Addressing sparse data challenges in recommendation systems: A systematic review of rating estimation using sparse rating data and profile enrichment techniquesIntelligent Systems with Applications10.1016/j.iswa.2024.20047425(200474)Online publication date: Mar-2025
  • (2024)Supervised matrix factorizationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693137(26752-26788)Online publication date: 21-Jul-2024
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  • (2024)Multi-Granularity Attention Mechanism for Recommendation Systems Based on Item Descriptions and ReviewsModeling and Simulation10.12677/mos.2024.13322213:03(2429-2440)Online publication date: 2024
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