Bidding friend recommendation system based on label position weight and self-learning

Abstract

The invention discloses a label position weight and self-learning based tendering and bidding good friend recommendation system. The system comprises a data source module, a label preprocessing module, a hot label library module, a label detection and storage module, a self-learning module and a result screening module. The recommendation system is developed and designed by taking a label position weight and self-learning model as a design thought. According to the recommendation system, a label system considering a label position weight is established, association rules in a purchase behavior process are mined by using an extended FP-Growth algorithm, and finally label based recommendation results and mining based recommendation results are combined and screened to form a recommendation result list. The system running data shows that compared with a conventional label based enterprise level tendering and bidding recommendation system, the label position weight and self-learning based tendering and bidding good friend recommendation system has the advantages that the effect of the label system is enhanced, the system potential association rules in which the label system is relatively weak are mined, and effective recommendation performance is provided.

Description

technical field [0001] The invention relates to the research field of friend recommendation for bidding, in particular to a friend recommendation system for bidding based on tag position weight and self-learning. Background technique [0002] In the online bidding system, the purchaser is responsible for initiating procurement requirements, and the system recommends a list of suitable suppliers according to the procurement requirements. Purchasers and suppliers can add friends or follow each other. The old tag-based bidding friend recommendation system more or less has the following deficiencies: (1) polysemous words, synonyms and other noise problems need to be dealt with, and the unconstrained grammar of the tag system may cause the above problems. (2) The procurement behavior process is not well considered. For example, if a company purchases a mobile phone, the recommendation system can recommend suppliers who sell mobile phone cases, mobile phone film and other products...

AI Technical Summary

Problems solved by technology

The old tag-based bidding friend recommendation system more or less has the following shortcomings: (1) polysemous words, synonyms and other noise problems need to be dealt with, and the unconstrained grammar of the tag system may cause the above problems

(3) The effectiveness of the recommendation depends on the denseness of the relationship between users, tags, and resources. Since users in the bidding system can only add tags to the information they publish, it may not be well achieved in practice. This dense requirement limits the recommendation performance of tag recommendation systems

Therefore, in the traditional tag-based bidding friend recommendation system, problems such as the randomness of personalized tags, the rough handling of tag position weights, and the quality limitations of system recommended tags limit the recommendation efficiency of the system, and the existing online bidding system Less consideration is given to the industrial chain relationship in the procurement process

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