Cross-domain recommendation, as a cutting-edge technology to settle data sparsity and
cold start problems, is gaining increasingly popular. Existing research paradigms primarily …

In the field of recommender systems, explainability remains a pivotal yet challenging aspect.
To address this, we introduce the Learning to eXplain Recommendations (LXR) framework …

Explanations in conventional recommender systems have demonstrated benefits in hel**
the user understand the rationality of the recommendations and improving the system's …

Music listening sessions often consist of sequences including repeating tracks. Modeling
such relistening behavior with models of human memory has been proven effective in …

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

The evaluation of recommender system algorithms depends on randomness, eg, during
randomly splitting data into training and testing data. We suspect that failing to account for …

Abstract Recommender Systems (RSs) may inadvertently perpetuate biases based on
protected attributes like gender, religion, or ethnicity. Left unaddressed, these biases can …

Popularity bias in music recommendation systems--where artists and tracks with the highest
listen counts are recommended more often--can also propagate biases along demographic …

Matrix factorization (MF) is a simple collaborative filtering technique that achieves superior
recommendation accuracy by decomposing the user–item interaction matrix into user and …

Cross-domain recommendation (CDR) aims to suggest items from new domains that align
with potential user preferences, based on their historical interactions. Existing methods …