تقرير
Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts
| العنوان: | Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts |
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| المؤلفون: | Milenkoski, Martin, Antognini, Diego, Musat, Claudiu |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Information Retrieval, Computer Science - Machine Learning |
| الوصف: | 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. Comment: 10 pages, 2 figures, 1 table, accepted at RecSys 2021 - Workshop on Cross-Market Recommendation (XMRec) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2104.12822 |
| رقم الأكسشن: | edsarx.2104.12822 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |