Personalized query recommendation using semantic factor model

In the field of query recommendation, the current techniques for semantic analysis technology can't meet the demands of users. In order to meet diverse needs, we improved the LDA model and designed a new query recommendation model based on collaborative filtering-Semantic Factor Model (SFM), which combines text information, user interest information and web source. First, we improved the LDA model from bag-of-word to bag-of-phrase to understand the topics expressed by users' frequently used sentences. The phrase bag model treats phrases as a whole and can capture more accurate query intent. Second, we use collaborative filtering to build an evaluation matrix between user interests and personalized expressions. Third, we designed a new scoring function that can recommend the top n resources to users. Finally, we conduct experiments on the AOL data set. The experimental results show that compared with other latest query recommendation techniques, SFM has higher recommendation quality.