Name
Abstract | ||
|---|---|---|
Users in recommender systems often express their opinions about different items by rating the items on a fixed rating scale. The rating information provided by the users is used by the recommender systems to generate personalized recommendations for them. Few recent research work on rating based recommender systems advocate the use of preference relations instead of absolute ratings in order to produce better recommendations. Use of preference relations for neighborhood based collaborative recommendation has been looked upon in recent literature. On the other hand, Matrix Factorization algorithms have been shown to perform well for recommender systems, specially when the data is sparse. In this work, we propose a matrix factorization based collaborative recommendation algorithm that considers preference relations. Experimental results show that the proposed method is able to achieve better recommendation accuracy over the compared baseline methods. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-31454-4_6 | UMAP |
Keywords | Field | DocType |
better recommendation,rating information,collaborative recommendation,personalized recommendation,better recommendation accuracy,fixed rating scale,preference relation,recommender system,matrix factorization,collaborative recommendation algorithm,absolute rating,rating scale | Recommender system,Preference relation,Information retrieval,Computer science,Matrix decomposition,Rating scale,Preference learning | Conference |
Citations | PageRank | References |
17 | 0.78 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maunendra Sankar Desarkar | 1 | 73 | 14.97 |
| Roopam Saxena | 2 | 17 | 0.78 |
| Sudeshna Sarkar | 3 | 423 | 210.58 |