Personalized recommendation based on heat bidirectional transfer

Personalized recommendation has become an increasing popular research topic, which aims to find future likes and interests based on users' past preferences. Traditional recommendation algorithms pay more attention to forecast accuracy by calculating first-order relevance, while ignore the importance of diversity and novelty that provide comfortable experiences for customers. There are some levels of contradictions between these three metrics, so an algorithm based on bidirectional transfer is proposed in this paper to solve this dilemma. In this paper, we agree that an object that is associated with history records or has been purchased by similar users should be introduced to the specified user and recommendation approach based on heat bidirectional transfer is proposed. Compared with the state-of-the-art approaches based on bipartite network, experiments on two benchmark data sets, Movielens and Netflix, demonstrate that our algorithm has better performance on accuracy, diversity and novelty. Moreover, this method does better in exploiting long-tail commodities and cold-start problem.