Time series anomaly detection is important for a wide range of research fields and
applications, including financial markets, economics, earth sciences, manufacturing, and …

In recent years, a variety of review-based recommender systems have been developed, with
the goal of incorporating the valuable information in user-generated textual reviews into the …

Industry devices (ie, entities) such as server machines, spacecrafts, engines, etc., are
typically monitored with multivariate time series, whose anomaly detection is critical for an …

Nowadays, the issue of information overload is gradually gaining exposure in the Internet of
Things (IoT), calling for more research on recommender system in advance for industrial IoT …

Recommendation plays an increasingly important role in our daily lives. Recommender
systems automatically suggest to a user items that might be of interest to her. Recent studies …

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

Large Language Models (LLMs) have demonstrated remarkable capabilities and have been
extensively deployed across various domains, including recommender systems. Prior …

We propose TrustSVD, a trust-based matrix factorization technique for recommendations.
TrustSVD integrates multiple information sources into the recommendation model in order to …

Abstract A Recommendation System (RS) is an intelligent computer based system which
provide valuable suggestions to the user and are used in several domains. Social media …

Recommender Systems are a prime example of the mainstream industry use of large-scale
machine learning and data mining. Diverse applications in areas such as e-commerce …