Name
Abstract | ||
|---|---|---|
The key to successful recommendations is to provide users with items likely to be consumed in the future. From realworld data, we observe that users' consumption patterns for items change over time. For example, users may no longer like some items they liked in the past. However, existing recommender systems model user's preference to items without considering how much users' interests in each item will sustain in the future. Thus, they often recommend less interesting items in the deployment time (i.e., test time). In this work, we propose a novel recommender system, called CRIS, that considers the change of users' interest in each item over time. More precisely, we first predict the interest sustainability of each item, that is, how likely each item will be consumed in the future. Then, our goal is to make users closer to the items with high interest sustainability scores in the representation space than those with low interest sustainability scores. We perform experiments on 11 real-world datasets to show the effectiveness of CRIS. We also show that considering the interest sustainability is indeed crucial for boosting the accuracy of recommendations. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICDM50108.2020.00028 | 2020 IEEE International Conference on Data Mining (ICDM) |
Keywords | DocType | ISSN |
n/a | Conference | 1550-4786 |
ISBN | Citations | PageRank |
978-1-7281-8317-6 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongmin Hyun | 1 | 7 | 2.82 |
| Junsu Cho | 2 | 5 | 2.13 |
| Chanyoung Park | 3 | 163 | 12.04 |
| Hwanjo Yu | 4 | 1715 | 114.02 |