关键词: 多特征融合, 异质信息, 搜索推荐算法, 用户推荐, 多视图机模型

Abstract: A heterogeneous information search recommendation algorithm based on multi feature fusion is proposed. The knowledge map technology is used to extract heterogeneous information features, select multi view machine model, learn and integrate local information of multi-feature heterogeneous information by using cooperative attention mechanism, get the importance vector of information through normalization of softmax function, and integrate all local information into the final node, obtain the score function. It uses the corresponding meta path interaction between users and commodities to obtain the global information recommendation optimization objective function of multiple label classification that is combined with the score function and the global information recommendation optimization objective function to obtain the final heterogeneous information search recommendation. The test results show that the algorithm can effectively recommend the information needed by users. The recommended complexity is low, and the normalized discount cumulative gain of searching for heterogeneous information is higher than 0. 35, which has strong recommendation performance and can be applied to solve the problem of information overload.

Key words: multi-feature fusion, heterogeneous information, search recommendation algorithm, user recommendation, multi-view machine model