Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …

This paper contributes improvements on both the effectiveness and efficiency of Matrix
Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing …

Maximizing the user-item engagement based on vectorized embeddings is a standard
procedure of recent recommender models. Despite the superior performance for item …

Cloud applications built on service-oriented architectures generally integrate a number of
component services to fulfill certain application logic. The changing cloud environment …

Abstract Traditional Collaborative Filtering algorithms for recommendation are designed for
stationary data. Likewise, conventional evaluation methodologies are only applicable in …

In crowdsourcing systems, tasks are distributed to networked people to complete such that a
company's production cost can be greatly reduced. Obviously, it is not efficient that the …

Abstract Recommender Systems (RSs) are software tools and techniques providing
suggestions of relevant items to users. These systems have received increasing attention …

In this paper, we present a hybrid real-time incremental stochastic gradient descent (RI-
SGD) updating technique for implicit feedback matrix factorization (MF) recommendation …

Video recommendation has attracted growing attention in recent years. However,
conventional techniques have limitations in real-time processing, accuracy or scalability for …