Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble
problem when users suffer the familiar, repeated, and even predictable recommendations …

Due to the expansion of e-business, the availability of products on the internet has massively
increased. Finding suitable stuff from the vast array of products available on the internet is a …

Session-based recommender systems (SBRSs) have shown superior performance over
conventional methods. However, they show limited scalability on large-scale industrial …

Collaborative Filtering (CF) recommender models highly depend on user-item interactions to
learn CF representations, thus falling short of recommending cold-start items. To address …

Meta-learning has been proven to be effective for the cold-start problem of recommender
systems. Many meta-learning recommender systems that are designed for the user cold-start …

Recommender systems are most successful for popular items and users with ample
interactions (likes, ratings etc.). This work addresses the difficult and underexplored case of …

In this paper we challenge the standard ways of how text-based recommender systems are
trained and evaluated. We highlight the necessity of focusing on long-tail users and items …

Recommender system research has oftentimes focused on approaches that operate on
large-scale datasets containing millions of user interactions. However, many small …

In one-class recommendation systems, the goal is to learn a model from a small set of
interacted users and items and then identify the positively-related user-item pairs among a …

Rating elicitation is a success element for recommender systems to perform well at cold-
starting, in which the systems need to recommend items to a newly arrived user with no prior …