Name
Abstract | ||
|---|---|---|
AbstractCollaborative filtering is a popular strategy in recommender systems area. This approach gathers users' ratings and then predicts what users will rate based on their similarity to other users. However, most of the collaborative filtering methods have faced problems such as sparseness and scalability. This paper presents a non-negative matrix factorisation method to alleviate these problems via decomposing rating matrix into user matrix and item matrix. This method tries to find two non-negative user matrix and item matrix whose product can well estimate the rating matrix. This approach proposes updated rules to learn the latent factors for factorising the rating matrix. The proposed method can estimate all the unknown ratings and its computational complexity is very low. Empirical studies on benchmark datasets show that the proposed method is more tolerant of the sparseness and scalability problems. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1177/0165551516654354 | Periodicals |
Keywords | Field | DocType |
collaborative filtering,latent factors,matrix factorisation,model-based recommender systems | Recommender system,Data mining,Collaborative filtering,Matrix factorisation,Matrix (mathematics),Computer science,Document-term matrix,Artificial intelligence,Machine learning,Empirical research,Computational complexity theory,Scalability | Journal |
Volume | Issue | ISSN |
43 | 4 | 0165-5515 |
Citations | PageRank | References |
2 | 0.38 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mehdi Hosseinzadeh Aghdam | 1 | 205 | 9.88 |
| Morteza Analoui | 2 | 124 | 24.94 |
| Peyman Kabiri | 3 | 132 | 11.94 |