Recommender systems have become an essential tool to help resolve the information
overload problem in recent decades. Traditional recommender systems, however, suffer …

In collaborative filtering recommender systems user's preferences are expressed as ratings
for items, and each additional rating extends the knowledge of the system and affects the …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

We propose an inductive matrix completion model without using side information. By
factorizing the (rating) matrix into the product of low-dimensional latent embeddings of rows …

What makes a good recommendation or good list of recommendations? Research into
recommender systems has traditionally focused on accuracy, in particular how closely the …

User-based collaborative filtering, a widely used nearest neighbour-based recommendation
technique, predicts an item's rating by aggregating its ratings from similar users. User …

Collaborative filtering (CF) is the most successful approach for personalized product or
service recommendations. Neighborhood based collaborative filtering is an important class …

Crowdsourcing information systems are socio-technical systems that provide informational
products or services by harnessing the diverse potential of large groups of people via the …

Recommender Systems are currently highly relevant for helping users deal with the
information overload they suffer from the large volume of data on the web, and automatically …

Recommender systems research is often based on comparisons of predictive accuracy: the
better the evaluation scores, the better the recommender. However, it is difficult to compare …