A common challenge for most current recommender systems is the cold-start problem. Due
to the lack of user-item interactions, the fine-tuned recommender systems are unable to …

In recommender systems, cold-start issues are situations where no previous events, eg,
ratings, are known for certain users or items. In this paper, we focus on the item cold-start …

Rating prediction is a classic problem underlying recommender systems. It is traditionally
tackled with matrix factorization. Recently, deep learning based methods, especially graph …

When building a recommender system, how can we ensure that all items are modeled well?
Classically, recommender systems are built, optimized, and tuned to improve a global …

The new user coldstart problem arises when a recommender system does not yet have any
information about a user. A common solution to it is to generate a profile by asking the user …

This paper introduces the status of social recommender system research in general and
collaborative filtering in particular. For the collaborative filtering, the paper shows the basic …

Any learning algorithm for recommendation faces a fundamental trade-off between
exploiting partial knowledge of a user» s interests to maximize satisfaction in the short term …

The item cold-start problem is inherent to collaborative filtering (CF) recommenders where
items and users are represented by vectors in a latent space. It emerges since CF …

The item cold-start problem is of a great importance in collaborative filtering (CF)
recommendation systems. It arises when new items are added to the inventory and the …

In large-scale assessments such as the ones encountered in MOOCs, a lot of usage data is
available because of the number of learners involved. Newcomers, that just arrive on a …