CB2CF: A Neural Multiview Content-to-Collaborative Filtering Model for Completely Cold Item Recommendations

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 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.

arXiv commentIn Proceedings of Recsys'19. ACM, Copenhagen, Denmark

References in corpus (6)

• Distributed Representations of Words and Phrases and their Compositionality
• Natural Language Processing (almost) from Scratch
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification
• Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content
• Bayesian Neural Word Embedding

Cited by in corpus (3)

• Contrastive Collaborative Filtering for Cold-Start Item Recommendation
• Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation
• Bootstrapping Contrastive Learning Enhanced Music Cold-Start Matching