Efficient personalized search service can bring great convenience in the production and life.With the rapid development of Internet technology,personalized search and recommendation task tends to become increasingly complex and is a hot research topic in the field of big data analysis.Personalized search and recommendation algorithms extensively collect user-generated content and obtain users'preference information.By using various machine learning,deep learning and other technologies,these algorithms build user interest preference models,predict users'behaviors,and recommend personalized items.It will improve users'experiences and commercial benefits.This paper introduces the description of the personalized search problem,and reviews the research work on the personalized search and recommendation algorithms for multi-source heterogeneous data.It includes traditional personalized search algorithms,personalized search algorithms with multi-source heterogeneous data and dynamic personalized search algorithms.It sortes out common data sets and evaluation indicators,and clarifies the practical application scenarios and development directions of the personalized search methods for multi-source heterogeneous data.It also discusses the deficiencies and challenges,which is expected to be helpful to researchers in related fields.
维普
万方数据