Deep interest shifting network with meta embeddings for fresh item recommendation
Article
Article Title | Deep interest shifting network with meta embeddings for fresh item recommendation |
---|---|
ERA Journal ID | 493 |
Article Category | Article |
Authors | Li, Zhao (Author), Wang, Haobo (Author), Ding, Donghui (Author), Hu, Shichang (Author), Zhang, Zhen (Author), Liu, Weiwei (Author), Gao, Jianliang (Author), Zhang, Zhiqiang (Author) and Zhang, Ji (Author) |
Journal Title | Complexity |
Article Number | 8828087 |
Number of Pages | 13 |
Year | 2020 |
Publisher | Hindawi Publishing Corporation |
Place of Publication | London, United Kingdom |
ISSN | 1076-2787 |
1099-0526 | |
Digital Object Identifier (DOI) | https://doi.org/10.1155/2020/8828087 |
Abstract | Nowadays, people have an increasing interest in fresh products such as new shoes and cosmetics. To this end, an E-commerce platform Taobao launched a fresh-item hub page on the recommender system, with which customers can freely and exclusively explore and purchase fresh items, namely, the New Tendency page. In this work, we make a first attempt to tackle the fresh-item recommendation task with two major challenges. First, a fresh-item recommendation scenario usually faces the challenge that the training data are highly deficient due to low page views. In this paper, we propose a deep interest-shifting network (DisNet), which transfers knowledge from a huge number of auxiliary data and then shifts user interests with contextual information. Furthermore, three interpretable interest-shifting operators are introduced. Second, since the items are fresh, many of them have never been exposed to users, leading to a severe cold-start problem. Though this problem can be alleviated by knowledge transfer, we further babysit these fully cold-start items by a relational meta-Id-embedding generator (RM-IdEG). Specifically, it trains the item id embeddings in a learning-to-learn manner and integrates relational information for better embedding performance. We conducted comprehensive experiments on both synthetic datasets as well as a real-world dataset. Both DisNet and RM-IdEG significantly outperform state-of-the-art approaches, respectively. Empirical results clearly verify the effectiveness of the proposed techniques, which are arguably promising and scalable in real-world applications. |
Keywords | fresh item recommendations |
ANZSRC Field of Research 2020 | 460501. Data engineering and data science |
460502. Data mining and knowledge discovery | |
Public Notes | Copyright © 2020 Zhao Li et al. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Byline Affiliations | Alibaba Group, China |
Zhejiang University, China | |
Wuhan University, China | |
Central South University, China | |
Zhejiang University of Finance and Economics, China | |
School of Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q63w7/deep-interest-shifting-network-with-meta-embeddings-for-fresh-item-recommendation
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