Name
Abstract | ||
|---|---|---|
Recommender systems have been demonstrated as a useful tool in assisting decision makings. Multi-criteria recommender systems take advantage user preferences in multiple criteria to produce better recommendations. In this paper, we propose a utility-based multi-criteria recommendation algorithm, in which we learn the user expectations by different learning-to-rank methods. Our experimental results based on the real-world data sets demonstrate the effectiveness of the proposed models.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3297280.3297641 | SAC |
Keywords | Field | DocType |
decision making, multi-criteria, recommender systems, utility | Recommender system,Data set,User expectations,Multiple criteria,Computer science,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-5933-7 | 2 | 0.39 |
References | Authors | |
0 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Zheng | 1 | 2 | 0.73 |