Research on Personalized Information Recommendation System Based on Subject Probability Distribution Model
[Purposes]The traditional personalized information recommendation system based on similar-ity calculation can not be widely used in the field of small and medium-sized news picture because of the high requirement of computing power and time delay of recommendation.This paper constructs a per-sonalized information recommendation system based on subject probability distribution model to help small and medium-sized news organizations to carry out personalized information accurate recommenda-tion service with lower cost.[Methods]The data collection technology was used to collect the original data corpus;LDA model training was used to classify the original text information;the user's subject por-trait was obtained by substituting user's information into LDA model training results;the personalized in-formation recommendation is realized by combining user subject portrait with text information classifica-tion.[Findings]the experimental results showed that the system had a strong recommendation time,which could reach the millisecond level.Compared with the reading records of users,the recommenda-tion results were in accordance with user's interest topics,and had a high recommendation accuracy.[Conclusions]The personalized information recommendation system based on topic probability distribu-tion model can help small and medium-sized news picture and information organizations to develop per-sonalized information accurate recommendation service with lower cost,which has certain application value.
LDA subject modelsubject probability distribution modelpersonalized information recom-mendationsystem design and implementation
万方数据
维普
