Interest-Related Item Similarity Model Based on Multimodal Data for Top-N Recommendation
Author | Lv, Junmei |
Author | Song, Bin |
Author | Guo, Jie |
Author | Du, Xiaojiang |
Author | Guizani, Mohsen |
Available date | 2020-08-18T08:34:43Z |
Publication Date | 2019 |
Publication Name | IEEE Access |
Resource | Scopus |
ISSN | 21693536 |
Abstract | Nowadays, the recommendation systems are applied in the fields of e-commerce, video websites, social networking sites, which bring great convenience to people's daily lives. The types of information are diversified and abundant in recommendation systems; therefore the proportion of unstructured multimodal data such as text, image, and video is increasing. However, due to the representation gap between different modalities, it is intractable to effectively use unstructured multimodal data to improve the efficiency of recommendation systems. In this paper, we propose an end-to-end multimodal interest-related item similarity model (multimodal IRIS) to provide recommendations based on the multimodal data source. Specifically, the multimodal IRIS model consists of three modules, i.e., multimodal feature learning module, the interest-related network (IRN) module, and item similarity recommendation module. The multimodal feature learning module adds knowledge sharing unit among different modalities. Then, IRN learns the interest relevance between target item and different historical items respectively. Finally, the multimodal feature learning, IRN, and item similarity recommendation modules are unified into an integrated system to achieve performance enhancements and to accommodate the addition or absence of different modal data. Extensive experiments on real-world datasets show that, by dealing with the multimodal data which people may pay more attention to when selecting items, the proposed multimodal IRIS significantly improves accuracy and interpretability on top-N recommendation task over the state-of-the-art methods. - 2013 IEEE. |
Sponsor | This work was supported in part by the National Natural Science Foundation of China under Grant 61772387 and Grant 61802296, in part by the Fundamental Research Funds for the Central Universities under Grant JB180101, in part by the China Postdoctoral Science Foundation under Grant 2017M620438, in part by the Fundamental Research Funds of Ministry of Education and China Mobile under Grant MCM20170202, and in part by the ISN State Key Laboratory. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | knowledge sharing unit multimodal data multimodal interest-related item similarity Top-N recommendation |
Type | Article |
Pagination | 12809-12821 |
Volume Number | 7 |
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Computer Science & Engineering [2284 items ]