Recommendation Model based on Mobile Commerce in Cloud Computing
Jia Wu, Bing LV, Wei CUI · 2020
To address the traditional collaborative filtering of data sparsity and cold start problem, a cloud user situational interest recommendation model under different trust information environment was proposed. A mobile commerce situational interest and rich trust information recommendation model trust relationship was introduced to solve the data sparseness problem existing in collaborative filtering algorithm, and processing methods to solve complex social network recommended by MapReduce data was proposed. Mobile commerce recommendation model of situational interest and sparse trust information was mainly devoted to solve trust less information available the reality circumstances lead to inaccurate problem based on the recommendations, specifically the situational interest similarity matrix and potential trust degree matrix were combined into a composite matrix. Then, we use the Resnick recommended formula and MapReduce data processing method to implement the recommender model.