Name
Abstract | ||
|---|---|---|
In Recommender Systems research, algorithms are often characterized as either Collaborative Filtering (CF) or Content Based (CB). CF algorithms are trained using a dataset of user explicit or implicit preferences while CB algorithms are typically based on item profiles. These approaches harness very different data sources hence the resulting recommended items are generally also very different. This paper presents a novel model that serves as a bridge from items content into their CF representations. We introduce a multiple input deep regression model to predict the CF latent embedding vectors of items based on their textual description and metadata. We showcase the effectiveness of the proposed model by predicting the CF vectors of movies and apps based on their textual descriptions. Finally, we show that the model can be further improved by incorporating metadata such as the movie release year and tags which contribute to a higher accuracy. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Information Retrieval | Recommender system,Data mining,Metadata,Embedding,Collaborative filtering,Information retrieval,Computer science,Regression analysis,Artificial intelligence,Machine learning |
DocType | Volume | Citations |
Journal | abs/1611.00384 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oren Barkan | 1 | 151 | 15.97 |
| Noam Koenigstein | 2 | 600 | 35.94 |
| Eylon Yogev | 3 | 1 | 3.73 |