research-article

Aspect-level sentiment capsule network for micro-video click-through rate prediction

Authors:

Jian WuAuthors Info & Claims

World Wide Web, Volume 24, Issue 4

Pages 1045 - 1064

https://doi.org/10.1007/s11280-020-00858-z

Published: 01 July 2021 Publication History

Abstract

Micro-videos, a new form of videos that are constrained in duration, gain significant popularity in recent years. The volume and rate of online micro-videos urgently calls for effective recommendation algorithms to help users find their interested ones. Although some previous works have investigated how to model users’ historical behaviors to predict the click-through rate of micro-videos, they are generally based on positive feedback only but overlook the negative which can help understand user preference at a finer granularity. The positive and negative feedback jointly imply the user’s different sentiments on different aspects, where each aspect is one component of a micro-video such as video_scene and video_subject. To this end, we propose an a spect-level s entiment cap sule network(ASCap) for micro-video click-through rate prediction by aggregating both positive and negative feedback, with an attempt to make the prediction more explainable. More specifically, an aspect-specific gating mechanism is firstly utilized to extract the aspect-level features from the target micro-video and the user’s positive and negative feedback. Then, in the following sentiment capsule network, the aspect-level features of the target micro-video are paired with those of positive and negative feedback respectively to identify their sentiments and form the sentiment capsules. Finally, the prediction layer is employed to calculate the overall click probability based on the sentiment capsules. Experimental results on two real-world micro-video datasets demonstrate that the proposed method significantly outperforms the state-of-the-art methods.

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Cited By

Shunpan LZhizhong ZGuozheng ZQianjin K(2024)SPECN:sequential patterns enhanced capsule network for sequential recommendationApplied Intelligence10.1007/s10489-024-06159-655:3Online publication date: 23-Dec-2024
https://dl.acm.org/doi/10.1007/s10489-024-06159-6
Zhang WTian BWang TYuan LJiang M(2024)Research on Micro-videos Recommendation Method Integrating Multimodal Data and User Multi-behaviorWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0570-5_1(3-16)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1007/978-981-96-0570-5_1
Tian CLiu MZhou D(2022)Preference-Aware Modality Representation and Fusion for Micro-video RecommendationPattern Recognition and Computer Vision10.1007/978-3-031-18907-4_26(330-343)Online publication date: 14-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-18907-4_26

Index Terms

Aspect-level sentiment capsule network for micro-video click-through rate prediction
1. Computing methodologies
2. Information systems

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cover image World Wide Web

World Wide Web Volume 24, Issue 4

Jul 2021

361 pages

ISSN:1386-145X

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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2020.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2021

Accepted: 14 December 2020

Revision received: 22 November 2020

Received: 04 August 2020

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Cited By

Shunpan LZhizhong ZGuozheng ZQianjin K(2024)SPECN:sequential patterns enhanced capsule network for sequential recommendationApplied Intelligence10.1007/s10489-024-06159-655:3Online publication date: 23-Dec-2024
https://dl.acm.org/doi/10.1007/s10489-024-06159-6
Zhang WTian BWang TYuan LJiang M(2024)Research on Micro-videos Recommendation Method Integrating Multimodal Data and User Multi-behaviorWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0570-5_1(3-16)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1007/978-981-96-0570-5_1
Tian CLiu MZhou D(2022)Preference-Aware Modality Representation and Fusion for Micro-video RecommendationPattern Recognition and Computer Vision10.1007/978-3-031-18907-4_26(330-343)Online publication date: 14-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-18907-4_26

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