Recommendation models that utilize unique identities (IDs for short) to represent distinct
users and items have been state-of-the-art (SOTA) and dominated the recommender …

Federated recommender system (FRS), which enables many local devices to train a shared
model jointly without transmitting local raw data, has become a prevalent recommendation …

Recommender systems play an important role in providing an engaging experience on
online music streaming services. However, the musical domain presents distinctive …

Performance of recommender systems (RS) relies heavily on the amount of training data
available. This poses a chicken-and-egg problem for early-stage products, whose amount of …

In this paper, we study the task of cold-start sequential recommendation, where new users
with very short interaction sequences come with time. We cast this problem as a few-shot …

Recommender systems have become an important instrument to connect people to
information. Sparse, complex, and rapidly growing data presents new challenges to …

Recently, one critical issue looms large in the field of recommender systems–there are no
effective benchmarks for rigorous evaluation–which consequently leads to unreproducible …

In a collaborative-filtering recommendation scenario, biases in the data will likely propagate
in the learned recommendations. In this paper we focus on the so-called mainstream bias …

Multimedia-based recommendation models learn user and item preference representation
by fusing both the user-item collaborative signals and the multimedia content signals. In real …

In recent years, the promotion of healthy habits, and especially diet-oriented habits, has
been one of the priority interests of our society. There are many apps created to count …