The rapid development of information and communication technologies has enabled a value
co-creation paradigm for develo** industrial products and services, where massive …

With the advent of the era of big data, the recommendation system has become an effective
solution to the problem of information overload. This paper takes the literature data related to …

Automated recommendations have become a pervasive feature of our online user
experience, and due to their practical importance, recommender systems also represent an …

Cold Start problems in recommender systems pose various challenges in the adoption and
use of recommender systems, especially for new item uptake and new user engagement …

Cross domain recommender systems have been increasingly valuable for hel**
consumers identify useful items in different applications. However, existing cross-domain …

Cross-Domain Recommendation (CDR) is a widely used approach for leveraging
information from domains with rich data to assist domains with insufficient data. A key …

In this paper, we present a strategy to provide users with explainable cross-domain
recommendations (CDR) that exploits large language models (LLMs). Generally speaking …

Collaborative filtering models based on matrix factorization and learned similarities using
Artificial Neural Networks (ANNs) have gained significant attention in recent years. This is, in …

Unsupervised Non-overlap** Cross-domain Recommendation (UNCR) is the task that
recommends source domain items to the target domain users, which is more challenging as …

Deep Learning and factorization-based collaborative filtering recommendation models have
undoubtedly dominated the scene of recommender systems in recent years. However …