In order to support the study of recent advances in recommender systems, this paper
presents an extended recommendation library consisting of eight packages for up-to-date …

Recently, embedding techniques have achieved impressive success in recommender
systems. However, the embedding techniques are data demanding and suffer from the cold …

The cold-start problem has been a long-standing issue in recommendation. Embedding-
based recommendation models provide recommendations by learning embeddings for each …

In this technical survey, the latest advancements in the field of recommender systems are
comprehensively summarized. The objective of this study is to provide an overview of the …

Next point-of-interest (POI) recommendation is a hot research field where a recent emerging
scenario, next POI to search recommendation, has been deployed in many online map …

Highly skewed long-tail item distribution is very common in recommendation systems. It
significantly hurts model performance on tail items. To improve tail-item recommendation …

User cold-start recommendation is a long-standing challenge for recommender systems due
to the fact that only a few interactions of cold-start users can be exploited. Recent studies …

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 …

Knowledge graphs (KGs), as a structured form of knowledge representation, have been
widely applied in the real world. Recently, few-shot knowledge graph completion (FKGC) …

A common challenge in personalized user preference prediction is the cold-start problem.
Due to the lack of user-item interactions, directly learning from the new users' log data …