Most recommender systems base their recommendations on implicit or explicit item-level
feedback provided by users. These item ratings are combined into a complex user model …

Recommender systems are typically evaluated in an offline setting. A subset of the available
user-item interactions is sampled to serve as test set, and some model trained on the …

A rank-r matrix X∈R^m*n can be written as a product UV^⊤, where U∈R^m*r and
V∈R^n*r. One could exploit this observation in optimization: eg, consider the minimization …

Automatic playlist continuation is a prominent problem in music recommendation. Significant
portion of music consumption is now done online through playlists and playlist-like online …

Traditional collaborative filtering techniques suffer from the data sparsity problem in practice.
That is, only a small proportion of all items in the recommender system occur in a user's …

Recommender systems are considered to be capable of predicting what the next product a
customer should purchase is. It is crucial to identify which customers are more suitable than …

Research and experimentation in various scientific fields are based on the observation,
analysis and benchmarking on datasets. The advancement of research and development …

Knowledge Graphs (KGs) serve as valuable auxiliary information to improve the accuracy of
recommendation systems. Previous methods have leveraged the knowledge graph to …

Reliability of a recommender system is extremely substantial for the continuity of the system.
Malicious users may harm the reliability of predictions by injecting fake profiles called …

We introduce an extension of the multi-instance learning problem where examples are
organized as nested bags of instances (eg, a document could be represented as a bag of …