Food recommender-systems are considered an effective tool to help users adjust their
eating habits and achieve a healthier diet. This paper aims to develop a new hybrid food …

Collaborative filtering based algorithms, including Recurrent Neural Networks (RNN), tend
towards predicting a perpetuation of past observed behavior. In a recommendation context …

The aggregate behaviors of users can collectively encode deep semantic information about
the objects with which they interact. In this paper, we demonstrate novel ways in which the …

The stock market investors aim at maximizing their investment returns. Stock
recommendation task is to recommend stocks with higher return ratios for the investors. Most …

Web systems that provide the same functionality usually share a certain amount of items.
This makes it possible to combine data from different websites to improve recommendation …

Traditional prediction models of rumor forwarding are based solely on explicit network
topology, and with no consideration for homogeneity and antagonism among multi-type …

Due to the ability of addressing the data sparsity and cold-start problems, Cross-Domain
Collaborative Filtering (CDCF) has received a significant amount of attention. Despite …

The spread of online rumor poses challenges to social peace and public order. Traditional
research on rumor diffusion commences from the rumor itself, without considering the …

Matrix factorization (MF) and its extended methodologies have been studied extensively in
the community of recommender systems in the last decade. Essentially, MF attempts to …

In recent years, undesirable disasters attacked the cities frequently, leaving heavy casualties
and serious economic losses. Meanwhile, disaster detection based on the Internet of Things …