- Title
- Edge data based trailer inception probabilistic matrix factorization for context-aware movie recommendation
- Creator
- Chen, Honglong; Li, Zhe; Wang, Zhu; Ni, Zhichen; Li, Junjian; Xu, Ge; Aziz, Abdul; Xia, Feng
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/190941
- Identifier
- vital:17726
- Identifier
-
https://doi.org/10.1007/s11280-021-00974-4
- Identifier
- ISSN:1386-145X (ISSN)
- Abstract
- The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data. The visual information in the movie trailer is a significant part of the movie recommender system. However, due to the complexity of visual information extraction, data sparsity cannot be remarkably alleviated by merely using the rough visual features to improve the rating prediction accuracy. Fortunately, the convolutional neural network can be used to extract the visual features precisely. Therefore, the end-to-end neural image caption (NIC) model can be utilized to obtain the textual information describing the visual features of movie trailers. This paper proposes a trailer inception probabilistic matrix factorization model called Ti-PMF, which combines NIC, recurrent convolutional neural network, and probabilistic matrix factorization models as the rating prediction model. We implement the proposed Ti-PMF model with extensive experiments on three real-world datasets to validate its effectiveness. The experimental results illustrate that the proposed Ti-PMF outperforms the existing ones. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
- Publisher
- Springer
- Relation
- World Wide Web Vol. 25, no. 5 (2022), p. 1863-1882
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021, The Author(s)
- Rights
- Open Access
- Subject
- 46 Information and computing sciences; Neural image caption; Probabilistic matrix factorization; Recommender systems; Visual feature extraction
- Full Text
- Reviewed
- Funder
- This work was supported in part by NSFC grants (No.61772551), the Open Project of Minjiang University (No.MJUKF-JK202003) and the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-003.
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