Open AccessResearch on University Innovation and Entrepreneurship Resource Database System Based on SSH2
Author(s) -
Libo Wu,
Lili Feng,
Jianna Yan
Publication year - 2022
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2022/1168796
Subject(s) - weighting , computer science , recommender system , resource (disambiguation) , mainstream , information resource , quality (philosophy) , similarity (geometry) , the internet , matrix (chemical analysis) , data mining , knowledge management , information retrieval , world wide web , artificial intelligence , medicine , computer network , philosophy , materials science , theology , epistemology , composite material , image (mathematics) , radiology
With the wide application of the Internet, the entrepreneurial resources of colleges and universities have grown at an exponential rate. With the rapid accumulation of this information, it is difficult for students to find what they are interested in from a large amount of information. To accurately recommend innovation and entrepreneurship resources, this paper proposes a recommendation algorithm based on user trust and a probability matrix. After obtaining the user trust data, the PMD (probability matrix decomposition) algorithm is used to complete the trust matrix and normalize it to get the similarity matrix. At the same time, the trust factor between users is added to the calculation process of the posterior probability, and the prediction score is obtained by maximizing the posterior probability. On this basis, the weights of users in the group are normalized, and the weighting strategy based on user interaction is used to integrate the preferences of group members to obtain the final recommendation results. When designing the recommendation system, the web system of the mainstream SSH2 framework is used to design, and the B/S structure of the entrepreneurial resource recommendation system platform is realized. Experimental results show that the proposed system has a higher recommendation quality compared with other recommended algorithms.