Name
Abstract | ||
|---|---|---|
ABSTRACTIn the business domain,bundling is one of the most important marketing strategies to conduct product promotions, which is commonly used in online e-commerce and offline retailers. Existing recommender systems mostly focus on recommending individual items that users may be interested in, such as the considerable research work on collaborative filtering that directly models the interaction between users and items. In this paper, we target at a practical but less explored recommendation problem named personalized bundle composition, which aims to offer an optimal bundle (i.e., a combination of items) to the target user. To tackle this specific recommendation problem, we formalize it as a combinatorial optimization problem on a set of candidate items and solve it within a neural combinatorial optimization framework. Extensive experiments on public datasets are conducted to demonstrate the superiority of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3474085.3475440 | International Multimedia Conference |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qilin Deng | 1 | 1 | 2.06 |
| Kai Wang | 2 | 1734 | 195.03 |
| Minghao Zhao | 3 | 0 | 1.01 |
| Runze Wu | 4 | 11 | 4.73 |
| Yu Ding | 5 | 0 | 0.68 |
| Zhene Zou | 6 | 0 | 1.01 |
| Yue Shang | 7 | 14 | 4.31 |
| Jianrong Tao | 8 | 51 | 11.96 |
| Changjie Fan | 9 | 57 | 21.37 |