Collaborative filtering using random walks of Markov chains

摘要

A collaborative filtering method first converts a relational database to a graph of nodes connected by edges. The relational database includes consumer attributes, product attributes, and product ratings. Statistics of a Markov chain random walk on the graph are determined. Then, in response to a query state, states of the Markov chain are determined according to the statistics to make a recommendation.