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

arXiv 2104.12822 ↗First posted 26 Apr 2021cs.IR cs.LGcited by 0

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Abstract (arXiv)

In this paper, we describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about 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 some 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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 43 references that are in the map. First posted on arXiv 26 Apr 2021.

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References · 20

  1. 2013Auto-Encoding Variational Bayescited by 3,980
  2. 2017Neural Collaborative Filteringcited by 1,108
  3. 2015Generating Sentences from a Continuous Spacecited by 607
  4. 2020S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximizationcited by 311
  5. 2018Variational Autoencoders for Collaborative Filteringcited by 308
  6. 2018Multimodal Generative Models for Scalable Weakly-Supervised Learningcited by 200
  7. 2019Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approachescited by 181
  8. 2019Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Modelscited by 177
  9. 2016Joint Multimodal Learning with Deep Generative Modelscited by 87
  10. 2018CoNet: Collaborative Cross Networks for Cross-Domain Recommendationcited by 81
  11. 2019DDTCDR: Deep Dual Transfer Cross Domain Recommendationcited by 71
  12. 2014Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictionscited by 66
8 more
  1. 2020Multimodal Generative Learning Utilizing Jensen-Shannon-Divergencecited by 48
  2. 2017Generative Models of Visually Grounded Imaginationcited by 44
  3. 2019DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patternscited by 33
  4. 2020HotelRec: a Novel Very Large-Scale Hotel Recommendation Datasetcited by 14
  5. 2018A Collective Variational Autoencoder for Top-N Recommendation with Side Informationcited by 9
  6. 2020Interacting with Explanations through Critiquingcited by 9
  7. 2013Utility Elicitation as a Classification Problemcited by 2
  8. 2018Domain-to-Domain Translation Model for Recommender Systemcited by 2

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