Abstract
Recommender systems leverage both content and user interactions to generate recommendations that fit users' preferences. The recent surge of interest in deep learning presents new opportunities for exploiting these two sources of information. To recommend items we propose to first learn a user-independent high-dimensional semantic space in which items are positioned according to their substitutability, and then learn a user-specific transformation function to transform this space into a ranking according to the user's past preferences. An advantage of the proposed architecture is that it can be used to effectively recommend items using either content that describes the items or user-item ratings. We show that this approach significantly outperforms state-of-the-art recommender systems on the MovieLens 1M dataset.
Original language | English |
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Title of host publication | Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, DLRS 2016 |
Place of Publication | New York, NY |
Publisher | Association for Computing Machinery (ACM) |
Pages | 23-28 |
Number of pages | 6 |
ISBN (Print) | 978-1-4503-4795-2 |
DOIs | |
Publication status | Published - 2016 |
Event | DLRS 2016: 1st Workshop on Deep Learning for Recommender Systems - Boston, MA, United States Duration: 15 Sept 2016 → 15 Sept 2016 |
Conference
Conference | DLRS 2016 |
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Abbreviated title | DLRS 2016 |
Country/Territory | United States |
City | Boston, MA |
Period | 15/09/16 → 15/09/16 |