Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts

• URL: http://arxiv.org/abs/2104.12822v1
• Date: Mon, 26 Apr 2021 18:56:04 GMT
• Title: Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts
• Authors: Martin Milenkoski, Diego Antognini, Claudiu Musat
• Abstract summary: We describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about the user preferences. The intuition is that user-item interactions in a source domain can augment the recommendation quality in a target domain. We present results on two widely-used datasets - Amazon and Yelp, which support the claim that holistic user preference knowledge leads to better recommendations.
• Score: 4.945620732698048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about the user preferences. We expand the variational autoencoder collaborative filtering from a single-domain to a multi domain setting. The intuition is that user-item interactions in a source domain can augment the recommendation quality in a target domain. The intuition can be taken to its extreme, where, in a cross-domain setup, the user history in a source domain is enough to generate high quality recommendations in a target one. We thus create a Product-of-Experts (POE) architecture for recommendations that jointly models user-item interactions across multiple domains. The method is resilient to missing data for one or more of the domains, which is a situation often found in real life. We present results on two widely-used datasets - Amazon and Yelp, which support the claim that holistic user preference knowledge leads to better recommendations. Surprisingly, we find that in select cases, a POE recommender that does not access the target domain user representation can surpass a strong VAE recommender baseline trained on the target domain. We complete the analysis with a study of the reasons behind this outperformance and an in-depth look at the resulting embedding spaces.

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