Name
Abstract | ||
|---|---|---|
In this paper we propose a novel framework for modeling the uniqueness of the user preferences for recommendation systems. User uniqueness is determined by learning to what extent the user's item preferences deviate from those of an "average user" in the system. Based on this framework, we suggest three different recommendation strategies that trade between uniqueness and conformity. Using two real item datasets, we demonstrate the effectiveness of our uniqueness based recommendation framework. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2484028.2484102 | SIGIR |
Keywords | Field | DocType |
user preference,real item datasets,user uniqueness,recommendation system,novel framework,average user,different recommendation strategy,item preferences deviate,recommendation framework,recommender systems | Recommender system,Uniqueness,Data mining,World Wide Web,Information retrieval,Computer science,Popularity,User modeling,Conformity | Conference |
Citations | PageRank | References |
1 | 0.36 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haggai Roitman | 1 | 314 | 32.07 |
| David Carmel | 2 | 2530 | 156.30 |
| Yosi Mass | 3 | 574 | 60.91 |
| Iris Eiron | 4 | 89 | 8.00 |