本文選題：信任知識(shí) + 隱反饋　； 參考：《武漢大學(xué)學(xué)報(bào)(理學(xué)版)》2017年02期

【摘要】：針對(duì)Web服務(wù)推薦現(xiàn)有技術(shù)缺乏顯式打分?jǐn)?shù)據(jù)缺點(diǎn),提出使用隱反饋知識(shí)進(jìn)行推薦的方法.該方法首先構(gòu)造一個(gè)偽評(píng)分生成器,將用戶隱反饋知識(shí)映射成為顯式打分.基于矩陣因子分解模型,將信任知識(shí)引入服務(wù)推薦過程,建立一種融合社交信任信息的服務(wù)推薦模型,有效提高了服務(wù)推薦性能.實(shí)驗(yàn)表明,本文提出的基于隱反饋的服務(wù)推薦方法預(yù)測(cè)性能優(yōu)于最近鄰方法和SVD++方法;同SVD++方法的性能對(duì)比實(shí)驗(yàn)表明,引入信任知識(shí)能夠進(jìn)一步提高服務(wù)推薦的性能,具有較好的實(shí)際應(yīng)用價(jià)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(xué)計(jì)算機(jī)與信息學(xué)院;武漢大學(xué)計(jì)算機(jī)學(xué)院;山東科技大學(xué)信息科學(xué)與工程學(xué)院;
【基金】：國(guó)家重點(diǎn)基礎(chǔ)研究發(fā)展計(jì)劃(973)(2014CB340600) 國(guó)家自然科學(xué)基金重點(diǎn)項(xiàng)目(6332019);國(guó)家自然科學(xué)基金資助項(xiàng)目(61173138,61272452) 福建省自然科學(xué)基金資助項(xiàng)目(2016J01285) 武漢大學(xué)軟件工程國(guó)家重點(diǎn)實(shí)驗(yàn)室開放課題(SKLSE2014-10-07)
【分類號(hào)】：TP391.3;TP393.09
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本文編號(hào)：1975407