本文選題：信任知識 + 隱反饋�。� 參考：《武漢大學學報(理學版)》2017年02期

【摘要】：針對Web服務推薦現(xiàn)有技術(shù)缺乏顯式打分數(shù)據(jù)缺點,提出使用隱反饋知識進行推薦的方法.該方法首先構(gòu)造一個偽評分生成器,將用戶隱反饋知識映射成為顯式打分.基于矩陣因子分解模型,將信任知識引入服務推薦過程,建立一種融合社交信任信息的服務推薦模型,有效提高了服務推薦性能.實驗表明,本文提出的基于隱反饋的服務推薦方法預測性能優(yōu)于最近鄰方法和SVD++方法;同SVD++方法的性能對比實驗表明,引入信任知識能夠進一步提高服務推薦的性能,具有較好的實際應用價值.
[Abstract]:In view of the lack of explicit data scoring in the current technology of Web service recommendation, a recommendation method using implicit feedback knowledge is proposed. Firstly, a pseudo-score generator is constructed to map the user's implicit feedback knowledge to explicit scoring. Based on the matrix factorization model, the trust knowledge is introduced into the service recommendation process, and a service recommendation model combining social trust information is established, which effectively improves the service recommendation performance. Experiments show that the performance of the proposed service recommendation method based on implicit feedback is superior to that of the nearest neighbor method and the SVD method, and the performance comparison with the SVD method shows that introducing trust knowledge can further improve the performance of service recommendation. It has good practical application value.
【作者單位】： 福建農(nóng)林大學計算機與信息學院;武漢大學計算機學院;山東科技大學信息科學與工程學院;
【基金】：國家重點基礎研究發(fā)展計劃(973)(2014CB340600) 國家自然科學基金重點項目(6332019);國家自然科學基金資助項目(61173138,61272452) 福建省自然科學基金資助項目(2016J01285) 武漢大學軟件工程國家重點實驗室開放課題(SKLSE2014-10-07)
【分類號】：TP391.3;TP393.09
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本文編號：1975407