Abstract Deep Neural Networks (DNN) based collaborative filtering has been successful in
recommending services by effectively generalizing graph-structured data. However, most …

Big data is typically characterized with 4V's: Volume, Velocity, Variety and Veracity. When it
comes to big graphs, these challenges become even more staggering. Each and every of …

Recommender systems, which are used to predict user requirements precisely, play a vital
role in the modern internet industry. As an effective tool with rich semantics, knowledge …

Overfitting is a common problem in machine learning, which means the model too closely fits
the training data while performing poorly in the test data. Among various methods of coping …

Sequential recommender systems (SRSs) are typically trained to predict the next item as the
target given its preceding (and succeeding) items as the input. Such a paradigm assumes …

The recommendation algorithm is a very important and challenging issue for a personal
recommender system. The collaborative filtering recommendation algorithm is one of the …

Social networks have proven to be effective for high-quality item recommendation. Most
social recommenders, however, are insufficient to capture the user preferences over items …

Product bundling represents a prevalent marketing strategy in both offline stores and e-
commerce systems. Despite its widespread use, previous studies on bundle …

To handle the limitations of collaborative filtering-based recommender systems, knowledge
graphs are getting attention as side information. However, there are several problems to …

This paper proposes RecLogic, a framework for improving the accuracy of machine learning
(ML) models for recommendation. It aims to enhance existing ML models with logic …