Context-aware recommender systems leverage the value of recommendations by exploiting
context information that affects user preferences and situations, with the goal of …

A substantial progress in development of new and efficient tensor factorization techniques
has led to an extensive research of their applicability in recommender systems field. Tensor …

Abstract We present the Temporal Graph Benchmark (TGB), a collection of challenging and
diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine …

Modeling sequential interactions between users and items/products is crucial in domains
such as e-commerce, social networking, and education. Representation learning presents …

RNNs have been shown to be excellent models for sequential data and in particular for data
that is generated by users in an session-based manner. The use of RNNs provides …

Session-based recommendations are highly relevant in many modern on-line services (eg e-
commerce, video streaming) and recommendation settings. Recently, Recurrent Neural …

We apply recurrent neural networks (RNN) on a new domain, namely recommender
systems. Real-life recommender systems often face the problem of having to base …

Real-life recommender systems often face the daunting task of providing recommendations
based only on the clicks of a user session. Methods that rely on user profiles--such as matrix …

With the recent surge of location based social networks (LBSNs), activity data of millions of
users has become attainable. This data contains not only spatial and temporal stamps of …

Recommendations can greatly benefit from good representations of the user state at
recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) …