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research-article

Empowering neural collaborative filtering with contextual features for multimedia recommendation

Authors:

Israr ur Rehman,

Muhammad Shehzad Hanif,

Cobbinah Bernard Mawuli,

Waqar AliAuthors Info & Claims

Multimedia Systems, Volume 29, Issue 4

Pages 2375 - 2388

https://doi.org/10.1007/s00530-023-01107-9

Published: 31 May 2023 Publication History

Abstract

A rapid growth in multimedia on various application platforms has made essential the provision of additional assistive technologies to handle information overload issues. Consequently, various multimedia recommendation systems have been developed by the research community. Among these Neural Collaborative Filtering (NCF) is one of the most commonly adopted recommendation frameworks. In this research, we argue that weighing contextual features can help the underlined learning model to develop a better understanding of a user’s behavior. We propose a Weighted Context-based Neural Collaborative Filtering (WNCF) model to supplement weighted contextual information into NCF for learning the user–item interaction function with respect to the different contextual conditions. We introduced an interactive mechanism for addressing the user ratings on items in various contextual situations. Learned contextual weights describe the importance of each item in specific contextual conditions. The proposed model can also assign different weights to the contextual conditions depending on their significance. We performed extensive experiments on three real-world datasets and the outcomes demonstrate the significance of our proposal in comparison with the state-of-the-art models. Empirical results highlight that integrating weighted contextual information with NCF has enhanced recommendation performance. Also, the in-depth analysis leads us toward a completely new research direction on context-aware recommender systems.

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Cited By

Yang YSun JAn G(2024)Exploring multi-dimensional interests for session-based recommendationMultimedia Systems10.1007/s00530-024-01437-230:5Online publication date: 13-Aug-2024
https://dl.acm.org/doi/10.1007/s00530-024-01437-2
Yan TCao LChai PYu S(2024)Improving collaborative filtering with SNE–GCN: a second-order neighbor enhanced graph convolutional networkMultimedia Systems10.1007/s00530-024-01338-430:3Online publication date: 27-May-2024
https://dl.acm.org/doi/10.1007/s00530-024-01338-4
Chen LGong ZXie HZhou M(2024)False Negative Sample Aware Negative Sampling for RecommendationAdvances in Knowledge Discovery and Data Mining10.1007/978-981-97-2262-4_16(195-206)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1007/978-981-97-2262-4_16

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Information & Contributors

Information

Published In

cover image Multimedia Systems

Multimedia Systems Volume 29, Issue 4

Aug 2023

584 pages

ISSN:0942-4962

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 31 May 2023

Accepted: 08 May 2023

Received: 03 January 2023

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Sichuan Science and Technology Program

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Cited By

Yang YSun JAn G(2024)Exploring multi-dimensional interests for session-based recommendationMultimedia Systems10.1007/s00530-024-01437-230:5Online publication date: 13-Aug-2024
https://dl.acm.org/doi/10.1007/s00530-024-01437-2
Yan TCao LChai PYu S(2024)Improving collaborative filtering with SNE–GCN: a second-order neighbor enhanced graph convolutional networkMultimedia Systems10.1007/s00530-024-01338-430:3Online publication date: 27-May-2024
https://dl.acm.org/doi/10.1007/s00530-024-01338-4
Chen LGong ZXie HZhou M(2024)False Negative Sample Aware Negative Sampling for RecommendationAdvances in Knowledge Discovery and Data Mining10.1007/978-981-97-2262-4_16(195-206)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1007/978-981-97-2262-4_16

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