Leveraging Large Language Models (LLMs) for recommendation has recently garnered
considerable attention, where fine-tuning plays a key role in LLMs' adaptation. However, the …

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 …

Most recommender systems adopt collaborative filtering (CF) and provide recommendations
based on past collective interactions. Therefore, the performance of CF algorithms degrades …

Federated recommendations (FRs), facilitating multiple local clients to collectively learn a
global model without disclosing user private data, have emerged as a prevalent architecture …

As online music consumption increasingly shifts towards playlist-based listening, the task of
playlist continuation, in which an algorithm suggests songs to extend a playlist in a …

For many recommender systems, the primary data source is a historical record of user clicks.
The associated click matrix is often very sparse, as the number of users× products can be far …

AS ession-B ased R ecommendation (SBR) seeks to predict users' future item preferences
by analyzing their interactions with previously clicked items. In recent approaches, G raph N …

Recommender systems (RS) traditionally leverage the large amount of user–item interaction
data. This exposes RS to a lower recommendation quality in cold-start scenarios, as well as …

Graph convolutions have been successfully applied to recommendation systems, utilizing
high-order collaborative signals present in the user-item interaction graph. This idea …

Video-sharing websites need to process the uploaded videos (eg, face recognition) before
distributing them to users. The timely processing of videos is critical for users to always enjoy …