Name
Abstract | ||
|---|---|---|
The presence or absence of one item in a recommendation list will affect the demand for other items because customers are often willing to switch to other items if their most preferred items are not available. The cross-item influence, called “peers effect”, has been largely ignored in the literature. In this paper, we develop a peers-aware recommender system, named PARS. We apply a ranking-based choice model to capture the cross-item influence and solve the resultant MaxMin problem with a decomposition algorithm. The MaxMin model solves for the recommendation decision in the meanwhile of estimating users’ preferences towards the items, which yields high-quality recommendations robust to input data variation. Experimental results illustrate that PARS outperforms a few frequently used methods in practice. An online evaluation with a flash sales scenario at Taobao also shows that PARS delivers significant improvements in terms of both conversion rates and user value.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3366423.3380013 | WWW '20: The Web Conference 2020
Taipei
Taiwan
April, 2020 |
Keywords | DocType | ISBN |
E-commerce, Recommender system, Ranking-based model, Demand substitution | Conference | 978-1-4503-7023-3 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huiqiang Mao | 1 | 0 | 0.68 |
| Yanzhi Li | 2 | 67 | 11.56 |
| Chenliang Li | 3 | 590 | 39.20 |
| Di Chen | 4 | 0 | 0.68 |
| Xiaoqing Wang | 5 | 38 | 8.28 |
| Yuming Deng | 6 | 6 | 2.44 |