Bin Li
ABSTRACT
In the development of e-commerce, Haiquan data gradually exerts its commercial value. Whether this value can be fully mined depends on the way of data mining and data utilization. Aiming at the background of massive big data demand, a personalized recommendation engine model based on collaborative filtering and content-based combined recommendation algorithms is proposed, and hot recommendation based on text similarity is tentatively incorporated. This model is proposed for the problems faced by big data recommendation. It includes two main modules: offline data calculation and online recommendation. Finally, a simulation experiment was carried out through the real data set of a certain domestic e-commerce and the Movie Lens data set to demonstrate the rationality of the improvement.
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Digital Library
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