Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

In this paper, we introduce a large scale multi-objective ranking system for recommending
what video to watch next on an industrial video sharing platform. The system faces many …

The emerging topic of sequential recommender systems has attracted increasing attention in
recent years. Different from the conventional recommender systems including collaborative …

Sequential recommender systems aim to model users' evolving interests from their historical
behaviors, and hence make customized time-relevant recommendations. Compared with …

Transformer models have achieved remarkable success in sequential recommender
systems (SRSs). However, computing the attention matrix in traditional dot-product attention …

Capturing users' precise preferences is a fundamental problem in large-scale recommender
system. Currently, item-based Collaborative Filtering (CF) methods are common matching …

Intelligence is the ability to learn from experience and use domain experts' knowledge to
adapt to new situations. In this context, an intelligent Recommender System should be able …

Performance of recommender systems (RS) relies heavily on the amount of training data
available. This poses a chicken-and-egg problem for early-stage products, whose amount of …

Sequential recommendation is the task of predicting the next items for users based on their
interaction history. Modeling the dependence of the next action on the past actions …