Name
Abstract | ||
|---|---|---|
We examine the case of over-specialization in recommender systems, which results from returning items that are too similar to those previously rated by the user. We propose Outside-The-Box (otb) recommendation, which takes some risk to help users make fresh discoveries, while maintaining high relevance. The proposed formalization relies on item regions and attempts to identify regions that are under-exposed to the user. We develop a recommendation algorithm which achieves a compromise between relevance and risk to find otb items. We evaluate this approach on the MovieLens data set and compare our otb recommendations against conventional recommendation strategies. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1639714.1639769 | RecSys |
Keywords | Field | DocType |
item region,recommendation algorithm,recommender system,high relevance,otb recommendation,conventional recommendation strategy,otb item,fresh discovery,movielens data,proposed formalization | Recommender system,Data mining,Computer science,MovieLens,Artificial intelligence,Compromise,Machine learning,Serendipity | Conference |
Citations | PageRank | References |
24 | 0.82 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeinab Abbassi | 1 | 74 | 4.90 |
| Sihem Amer-Yahia | 2 | 2400 | 176.15 |
| Laks V. S. Lakshmanan | 3 | 6216 | 696.78 |
| Sergei Vassilvitskii | 4 | 2750 | 139.31 |
| Cong Yu | 5 | 1366 | 71.75 |