本文選題：社會化商務 + 個性化推薦�。� 參考：《華南理工大學》2016年碩士論文

【摘要】：Web2.0 時代下,社交化應用的出現(xiàn)使互聯(lián)網(wǎng)生活方式產生了巨大的變革,其商業(yè)潛力在電商企業(yè)的努力探索下正不斷地釋放,社會化商務成為了電子商務發(fā)展的新方向。社會化商務環(huán)境中的信任關系深刻地影響著消費者的購買決策,成為了支撐網(wǎng)絡商務活動開展的重要因素。同時,該環(huán)境下豐富的數(shù)據(jù)資源也為研究信任關系提供了數(shù)據(jù)基礎。因此,為了提高傳統(tǒng)推薦方法的推薦準確性,本文將充分地挖掘社交和項目評分數(shù)據(jù),研究用戶之間的信任關系,并尋找合理的方法將信任融入到推薦方法中。以社交數(shù)據(jù)為基礎,本文提出了基于社會化關系與信任傳播的協(xié)同過濾推薦方法。首先,為了使社交關注數(shù)據(jù)能更加真實可信地表達信任關系,本文將用戶社交屬性數(shù)據(jù)計算了用戶可信評分,并將其對社交關注數(shù)據(jù)進行了可信量化。其次,本文充分挖掘了可信量化后的社交關注數(shù)據(jù),結合同引分析方法提出了一種同引信任關系。結合直接、間接和同引信任后的綜合信任關系有助于相似度計算過程中準確地尋找信任鄰居。最后,針對評分較少的用戶之間利用傳統(tǒng)方法計算相似度不準確的問題,本文提出了基于信任傳播的相似度計算方法。在Epinions和大眾點評數(shù)據(jù)集上的實驗結果表明:本文研究的社會化信任關系和相似性計算方法具有一定的合理有效性。與相關推薦方法相比,本文研究方法在MAE等評價指標上皆具出色的表現(xiàn),使推薦效果得到了進一步提高。以項目評分數(shù)據(jù)為基礎,通過充分利用項目評分數(shù)據(jù)對隱性信任關系進行挖掘,本文提出了基于項目評分與信任挖掘的協(xié)同過濾推薦方法。通常對隱性信任的構建考慮的用戶行為特征較少,而且大部分未考慮到信任傳遞特性。另外,已有研究大多數(shù)都局限于基于用戶的隱性信任推薦,缺少了從用戶對項目的隱性信任角度進行研究。因此,本文將充分地考慮用戶在項目評分上的特征以及信任的弱傳遞特性,從基于用戶和基于項目兩種角度挖掘用戶之間的隱性信任關系,并將兩者融合形成綜合隱性信任推薦方法。在Movielens和大眾點評數(shù)據(jù)集上的實驗結果表明:本文研究的隱性信任關系相對具有合理有效性。相較于傳統(tǒng)推薦方法,本文研究方法在MAE等評價指標上表現(xiàn)更為出色,能更加準確地為用戶推薦興趣相符的項目。
[Abstract]:In the era of Web 2.0, the emergence of social application has brought about a great change in the way of life on the Internet, and its commercial potential is being continuously released under the efforts of e-commerce enterprises, and socialized commerce has become a new direction of the development of electronic commerce. The trust relationship in the social business environment has a profound impact on consumers' purchase decisions and has become an important factor supporting the development of online business activities. At the same time, the rich data resources in this environment also provide the data basis for the study of trust relationship. Therefore, in order to improve the accuracy of traditional recommendation methods, this paper will fully mine social and item scoring data, study the trust relationship between users, and find a reasonable way to integrate trust into recommendation methods. Based on social data, this paper proposes a collaborative filtering recommendation method based on social relationship and trust propagation. Firstly, in order to make the social concern data express the trust relationship more truthfully, this paper calculates the user trust score and quantifies the social concern data. Secondly, this paper fully excavates the social concern data after trusted quantization, and proposes a kind of co-citation trust relationship combined with co-citation analysis method. The combination of direct, indirect and cocitation trust relationship can help to find the trust neighbor accurately in the process of similarity calculation. Finally, aiming at the problem that the traditional method is not accurate in calculating similarity between users with less score, this paper proposes a similarity calculation method based on trust propagation. The experimental results on Epinions and Dianping datasets show that the socialized trust relationships and similarity calculation methods studied in this paper are reasonable and effective. Compared with the related recommendation methods, the research methods in this paper have excellent performance on the evaluation indexes such as mae, which makes the recommendation effect further improved. Based on item scoring data, this paper proposes a collaborative filtering recommendation method based on item score and trust mining. In general, implicit trust construction takes less user behavior characteristics into account, and most of them do not consider trust transfer characteristics. In addition, most of the previous studies are limited to the implicit trust recommendation based on users, and lack of research from the point of view of users' implicit trust to the project. Therefore, this paper will fully consider the characteristics of users in item scoring and the weak transfer of trust, mining the implicit trust relationship between users from the perspective of users and project-based. And the combination of the two to form a comprehensive recessive trust recommendation method. The experimental results on Movielens and Dianping datasets show that the implicit trust relationship studied in this paper is relatively reasonable and effective. Compared with the traditional recommendation method, the research method in this paper is more excellent in the evaluation index of mae, and can more accurately recommend items of interest to users.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：F724.6

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