Recommender systems have been widely deployed in various real-world applications to
help users identify content of interest from massive amounts of information. Traditional …

While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …

To alleviate the problem of information explosion, recommender systems are widely
deployed to provide personalized information filtering services. Usually, embedding tables …

Incorporating knowledge graph (KG) into recommender system is promising in improving the
recommendation accuracy and explainability. However, existing methods largely assume …

Since it can effectively address the problem of sparsity and cold start of collaborative
filtering, knowledge graph (KG) is widely studied and employed as side information in the …

Many predictive tasks of web applications need to model categorical variables, such as user
IDs and demographics like genders and occupations. To apply standard machine learning …

In recent years, deep neural networks have yielded immense success on speech
recognition, computer vision and natural language processing. However, the exploration of …

Factorization Machines (FMs) are a supervised learning approach that enhances the linear
regression model by incorporating the second-order feature interactions. Despite …

Multimedia content is dominating today's Web information. The nature of multimedia user-
item interactions is 1/0 binary implicit feedback (eg, photo likes, video views, song …

Item-to-item collaborative filtering (aka. item-based CF) has been long used for building
recommender systems in industrial settings, owing to its interpretability and efficiency in real …