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 preferences while CB algorithms are typically based on item profiles. These approaches harness different data sources and therefore the resulting recommended items are generally very different. This paper presents the CB2CF, a deep neural multiview model that serves as a bridge from items content into their CF representations. CB2CF is a “real-world” algorithm designed for Microsoft Store services that handle around a billion users worldwide. CB2CF is demonstrated on movies and apps recommendations, where it is shown to outperform an alternative CB model on completely cold items.
|Title of host publication||RecSys 2019 - 13th ACM Conference on Recommender Systems|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||9|
|State||Published - 10 Sep 2019|
|Event||13th ACM Conference on Recommender Systems, RecSys 2019 - Copenhagen, Denmark|
Duration: 16 Sep 2019 → 20 Sep 2019
|Name||RecSys 2019 - 13th ACM Conference on Recommender Systems|
|Conference||13th ACM Conference on Recommender Systems, RecSys 2019|
|Period||16/09/19 → 20/09/19|
Bibliographical notePublisher Copyright:
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- Cold item recommendations
- Multiview Representation Learning