Narrative-driven recommendation (NDR) presents an information access problem where
users solicit recommendations with verbose descriptions of their preferences and context, for …

Collaborative Filtering (CF) models, despite their great success, suffer from severe
performance drops due to popularity distribution shifts, where these changes are ubiquitous …

There were fierce debates on whether the non-linear embedding propagation of GCNs is
appropriate to GCN-based recommender systems. It was recently found that the linear …

Abstract We leverage the Neural Tangent Kernel and its equivalence to training infinitely-
wide neural networks to devise $\infty $-AE: an autoencoder with infinitely-wide bottleneck …

Ranking algorithms in recommender systems influence people to make decisions.
Conventional ranking algorithms based on implicit feedback data aim to maximize the utility …

The recommendation system makes recommendations based on the preferences of the
users. These user preferences usually come from the user's basic information, item rating …

We study the problem of recommending relevant products to users in relatively resource-
scarce markets by leveraging data from similar, richer in resource auxiliary markets. We …

We address the multimedia recommendation problem, which utilizes items' multimodal
features, such as visual and textual modalities, in addition to interaction information. While a …

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

There has been sustained interest from both academia and industry throughout the years
due to the importance and practicability of recommendation systems. However, several …