Recommender resources based on acquiring user's requirement and exploring user's preference with Word2Vec model in web service. Issue 3 (29th July 2019) - Qin, Jiwei; Jiang, Yunpeng
Traditional recommender algorithms mainly use structured data (resource tag, user feature etc.) to depict the user preference and ignore the semantic relations of resources. In this paper, we present a new idea for acquiring user's requirement and exploring user's preference with Word2Vec model (RP-Word2Vec) to find the interested and personal resource in the web service. We use Word2Vec model to measure the sentiment among keywords and acquire user's requirement as accurately as possible; and we treat resources as the input of Word2Vec model based on history behaviours and adopt a semantic similarity measuring process to recommend interested and personal resource for the user. The experiments results that the presented RP-Word2Vec supports more effective.