The ongoing rapid expansion of the Internet greatly increases the necessity of effective
recommender systems for filtering the abundant information. Extensive research for …

Purpose This paper aims to identify, evaluate and integrate the findings of all relevant and
high-quality individual studies addressing one or more research questions about …

Latent factor analysis (LFA) via stochastic gradient descent (SGD) is highly efficient in
discovering user and item patterns from high-dimensional and sparse (HiDS) matrices from …

Based on the user-tag-object tripartite graphs, we propose a recommendation algorithm that
makes use of social tags. Besides its low cost of computational time, the experimental results …

In the past decade, Social Tagging Systems have attracted increasing attention from both
physical and computer science communities. Besides the underlying structure and dynamics …

Recommender systems are programs that aim to provide personalized recommendations to
users for specific items (eg music, books) in online sharing communities or on e-commerce …

Many researchers have introduced tag information to recommender systems to improve the
performance of traditional recommendation techniques. However, user-defined tags will …

Graph-based recommendation approaches use a graph model to represent the
relationships between users and items, and exploit the graph structure to make …

The process of heat conduction has recently found application in personalized
recommendation [Zhou, Proc. Natl. Acad. Sci. USA 107, 4511 (2010) PNASA6 0027-8424 …

Recommender systems have been regarded as gaining a more significant role with the
emergence of the first research article on collaborative filtering (CF) in the mid-1990s. CF …