Name
Abstract | ||
|---|---|---|
Word embeddings techniques have attracted a lot of attention recently due to their effectiveness in different tasks. Inspired by the continuous bag-of-words model, we present prefs2vec, a novel embedding representation of users and items for memory-based recommender systems that rely solely on user–item preferences such as ratings. To improve the performance and prevent overfitting, we use a variant of dropout as regularization, which can leverage existent word2vec implementations. Additionally, we propose a procedure for incremental learning of embeddings that boosts the applicability of our proposal to production scenarios. The experiments show that prefs2vec with a standard memory-based recommender system outperforms all the state-of-the-art baselines in terms of ranking accuracy, diversity, and novelty. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.engappai.2019.06.020 | Engineering Applications of Artificial Intelligence |
Keywords | Field | DocType |
Embedding vector,User representation,Item representation,Collaborative filtering,Recommender systems | Recommender system,Collaborative filtering,Embedding,Ranking,Computer science,Regularization (mathematics),Artificial intelligence,Overfitting,Word2vec,Novelty,Machine learning | Journal |
Volume | ISSN | Citations |
85 | 0952-1976 | 2 |
PageRank | References | Authors |
0.38 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Valcarce | 1 | 54 | 8.51 |
| Alfonso Landin | 2 | 2 | 1.06 |
| Javier Parapar | 3 | 188 | 25.91 |
| Alvaro Barreiro | 4 | 226 | 22.42 |