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| Content Provider | ACM Digital Library |
|---|---|
| Author | Gama, João Vinagre, João Jorge, Alípio Mário |
| Abstract | Many online communities and services continuously generate data that can be used by recommender systems. When explicit ratings are not available, rating prediction algorithms are not directly applicable. Instead, data consists of positive-only user-item interactions, and the task is therefore not to predict ratings, but rather to predict good items to recommend -- item prediction. One particular challenge of positive-only data is how to interpret absent user-item interactions. These can either be seen as negative or as unknown preferences. In this paper, we propose a recency-based scheme to perform negative preference imputation in an incremental matrix factorization algorithm designed for streaming data. Our results show that this approach substantially improves the accuracy of the baseline method, outperforming both classic and state-of-the-art algorithms. |
| Starting Page | 963 |
| Ending Page | 965 |
| Page Count | 3 |
| File Format | |
| ISBN | 9781450331968 |
| DOI | 10.1145/2695664.2695998 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-04-13 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Incremental Data streams Recommender systems |
| Content Type | Text |
| Resource Type | Article |
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