Collaborative filtering recommendation algorithm based on trust relationship in large data
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In recent years, the arrival of big data era has brought new opportunities and challenges to collaborative filtering recommendation system. Introducing trust into traditional collaborative filtering algorithm, a collaborative filtering recommendation algorithm based on improved trust is proposed. However, there is still room for research and improvement on how to expand the limited social relations and how to reveal the impact of user interaction on user characteristics. Hadoop, as an open source metaphysical computing platform, realizes the function of cloud computing, which is widely used by researchers. Therefore, the author studies the collaborative filtering recommendation algorithm based on trust relationship in big data. In order to solve the data sparseness problem commonly associated with the collaborative filtering recommendation algorithm, the trust relationship is combined with the traditional collaborative filtering algorithm. Through the transferability of trust relationship, the relationship between trust degree and similarity is used to improve the data sparsity problem, and a collaborative filtering recommendation algorithm based on trust model is formed. However, the algorithm still needs to be improved in terms of time performance. The next step is to combine the clustering algorithm to make the recommendation algorithm further improve the time performance.
Big data, trust relationship, collaborative filtering recommendation algorithm