Personalized Recommendations Based on Sentimental Interest Community Detection

Innovation Detection Based on User-Interest Ontology of Blog Community

Recently, the use of blogs has been a remarkable means to publish user interests. In order to find suitable information resources from a large amount of blog entries which are published every day, we need an information filtering technique to automatically transcribe user interests to a user profile in detail. In this paper, we first classify user blog entries into service domain ontologies and...

A Personalized Interest-Forgetting Markov Model for Recommendations

Intelligent item recommendation is a key issue in AI research which enables recommender systems to be more “human-minded” when generating recommendations. However, one of the major features of human — forgetting, has barely been discussed as regards recommender systems. In this paper, we considered people’s forgetting of interest when performing personalized recommendations, and brought forward...

Overlapping Community Detection in Social Networks Based on Stochastic Simulation

Community detection is a task of fundamental importance in social network analysis. Community structures enable us to discover the hidden interactions among the network entities and summarize the network information that can be applied in many applied domains such as bioinformatics, finance, e-commerce and forensic science. There exist a variety of methods for community detection based on diffe...

Community Detection Using Time-Dependent Personalized PageRank

Local graph diffusions have proven to be valuable tools for solving various graph clustering problems. As such, there has been much interest recently in efficient local algorithms for computing them. We present an efficient local algorithm for approximating a graph diffusion that generalizes both the celebrated personalized PageRank and its recent competitor/companion the heat kernel. Our algor...

Community Detection & Network Analysis for Beer Recommendations