本文選題：用戶興趣建模 + 社交網(wǎng)絡(luò)　； 參考：《天津大學(xué)》2016年碩士論文

【摘要】：隨著信息技術(shù)的發(fā)展和經(jīng)濟(jì)社會(huì)信息化進(jìn)程的加快,電子商務(wù)進(jìn)入繁榮發(fā)展時(shí)期。電子商務(wù)中的商品規(guī)模急劇增加,用戶發(fā)現(xiàn)滿意商品的困難增大,“信息過載”等問題日益嚴(yán)重。個(gè)性化推薦技術(shù)能夠基于用戶在網(wǎng)上的行為挖掘用戶興趣,從而主動(dòng)地向用戶推薦其可能感興趣的商品。準(zhǔn)確獲取用戶的興趣,是個(gè)性化推薦的基礎(chǔ),在推薦系統(tǒng)中發(fā)揮著核心作用。用戶興趣一般不是單一的,而是呈現(xiàn)多樣化狀態(tài)。此外,隨著社交網(wǎng)絡(luò)的發(fā)展,有不少研究指出用戶和其社交好友之間存在相似的興趣,用戶也更加信賴來(lái)自社交好友的推薦。如果能夠結(jié)合社交網(wǎng)絡(luò)信息,推薦性能可能會(huì)得到進(jìn)一步的提升。本文結(jié)合用戶多興趣模型和社交網(wǎng)絡(luò)相關(guān)理論,應(yīng)用神經(jīng)網(wǎng)絡(luò)技術(shù),針對(duì)用戶興趣建模展開研究。本文介紹了個(gè)性化推薦的原理與技術(shù),綜述了個(gè)性化推薦的研究現(xiàn)狀;提出了一種用戶興趣模型,充分考慮用戶的多種興趣,將其與協(xié)同過濾算法進(jìn)行結(jié)合后能夠針對(duì)用戶的不同興趣分別進(jìn)行推薦;在多興趣模型的基礎(chǔ)上,結(jié)合社交網(wǎng)絡(luò)信息,引入社交好友興趣來(lái)增強(qiáng)用戶興趣模型;在多個(gè)數(shù)據(jù)集上進(jìn)行實(shí)驗(yàn)驗(yàn)證,實(shí)驗(yàn)結(jié)果表明本文算法推薦準(zhǔn)確性較高、多樣性較強(qiáng),并且在一定程度上能夠有效緩解用戶冷啟動(dòng)問題。
[Abstract]:With the development of information technology and the acceleration of economic and social informatization process, electronic commerce has entered a prosperous period. In electronic commerce, the scale of goods increases rapidly, the difficulty of users finding satisfied goods increases, and the problem of "information overload" becomes more and more serious. Personalized recommendation technology can be based on the behavior of users on the Internet mining user interest, so as to actively recommend to the user may be interested in the goods. Accurate acquisition of user interest is the basis of personalized recommendation and plays a central role in the recommendation system. User interest is generally not a single, but presents a diversified state. In addition, with the development of social networks, many studies have pointed out that users and their social friends have similar interests, and users rely more on recommendations from social friends. If you can combine social network information, recommendation performance may be further improved. Based on the theory of user multi-interest model and social network, this paper applies neural network technology to research user interest modeling. This paper introduces the principle and technology of personalized recommendation, summarizes the research status of personalized recommendation, and proposes a model of user interest, which fully considers the various interests of users. Based on the multi-interest model and the social network information, the user interest model can be enhanced by introducing the social friend interest. Experimental results on multiple data sets show that the proposed algorithm has high accuracy and diversity, and can effectively alleviate the cold start problem of users to a certain extent.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.3
，

本文編號(hào)：1931261