Name
Abstract | ||
|---|---|---|
Research in the area of recommender systems is largely focused on the value such a system creates for the users, by helping them finding items they are interested in. This is usually done by learning to rank the recommendable items based on their assumed relevance for each user. The implicit underlying goal often is that this personalization positively affects users in different positive ways, e.g., by making their search and decision processes easier or by helping them discover new things [3].
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3298689.3347060 | Proceedings of the 13th ACM Conference on Recommender Systems |
Keywords | Field | DocType |
evaluation, impact of recommender systems | Recommender system,World Wide Web,Computer science,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-6243-6 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oren Sar Shalom | 1 | 20 | 7.74 |
| Dietmar Jannach | 2 | 1847 | 130.74 |
| Ido Guy | 3 | 1444 | 85.72 |