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| Content Provider | ACM Digital Library |
|---|---|
| Author | Kalloori, Saikishore |
| Abstract | Most of the present research and application of Recommender Systems is based on the usage of preferences derived from absolute evaluations, such as user ratings or clicks. However, this type of preferences has few disadvantages, e.g., if most of the user rated items are 5 stars, then it is difficult to understand which item the user prefers among them. In this research work, we focus on pairwise preferences as an alternative way for modeling user preferences and compute recommendations. In our scenario, users provide pair scores for a set of item pairs, indicating which item, and to what extent, is preferred. In this Ph.D research, we aim at developing intelligent user interfaces that optimally combine ratings with pairwise preferences. Furthermore, we aim at identifying specific conditions/situations where pairwise preferences elicitation is meaningful and beneficial. |
| Starting Page | 169 |
| Ending Page | 172 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450348935 |
| DOI | 10.1145/3030024.3038278 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-03-07 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Pairwise preferences Recommender systems Ratings |
| Content Type | Text |
| Resource Type | Article |
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