2018 Proceeding
No abstract available.
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Deep Learning from Logged Interventions
Every time a system places an ad, presents a search ranking, or makes a recommendation, we can think about this as an intervention for which we can observe the user's response (e.g. click, dwell time, purchase). Such logged intervention data is one of ...
Delayed learning, multi-objective optimization, and whole slate generation in recommender systems
In this talk, I'll cover three areas our team at DeepMind have been working on in recommender systems. First, in recommender systems often we observed delayed signals such as longer term user engagement, user conversions, and delays may simply result ...
A Collective Variational Autoencoder for Top-N Recommendation with Side Information
Recommender systems have been studied extensively due to their practical use in real-world scenarios. Despite this, generating effective recommendations with sparse user ratings remains a challenge. Side information has been widely utilized to address ...
Item Recommendation with Variational Autoencoders and Heterogeneous Priors
In recent years, Variational Autoencoders (VAEs) have been shown to be highly effective in both standard collaborative filtering applications and extensions such as incorporation of implicit feedback. We extend VAEs to collaborative filtering with side ...
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling,...
Knowledge-aware Autoencoders for Explainable Recommender Systems
Recommender Systems have been widely used to help users in finding what they are looking for thus tackling the information overload problem. After several years of research and industrial findings looking after better algorithms to improve accuracy and ...
Index Terms
- Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems