The recommendation system plays an integral role in our daily lives, from movies to medical
treatment. However, designing an efficient recommendation system is a complex task that …

Traditional recommendation systems, relying on single-domain data, often struggle with
sparse data or new user scenarios. Cross-domain Recommendation (CDR) systems …

Cold-start problem is still a very challenging problem in recommender systems. Fortunately,
the interactions of the cold-start users in the auxiliary source domain can help cold-start …

Recommender systems are widely used in various real-world applications, but they often
encounter the persistent challenge of the user cold-start problem. Cross-domain …

Data sparsity and the cold start problem significantly impede the advancement of
recommendation systems. Cross-domain recommendation (CDR) seeks to alleviate these …

Cold-start problem is one of the most challenging problems for recommender systems. One
promising solution to this problem is cross-domain recommendation (CDR) which leverages …

Cross-domain recommendation (CDR) plays a critical role in alleviating the sparsity and
cold-start problem and substantially boosting the performance of recommender systems …

Cross-Domain Recommendation (CDR) is a promising paradigm inspired by transfer
learning to solve the cold-start problem in recommender systems. Existing state-of-the-art …

The core idea of cross-domain recommendation is to alleviate the problem of data scarcity.
Previous methods have made brilliant successes. However, many of them mainly focus on …

Data sparsity and cold-start problems are persistent challenges in recommendation systems.
Cross-domain recommendation (CDR) is a promising solution that utilizes knowledge from …