本文選題：協(xié)同過濾 + 網(wǎng)絡(luò)購物�。� 參考：《東南大學(xué)》2017年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)的發(fā)展,網(wǎng)絡(luò)商品各色各樣,種類越來越繁多,在網(wǎng)絡(luò)購物多重不安定因素下,人們在網(wǎng)絡(luò)有效購物也越來越困難,購物效率也會隨之下降,制約我國電子商務(wù)的發(fā)展。隨著網(wǎng)上信息數(shù)量和商品種類的急速增長對推薦系統(tǒng)提出了嚴(yán)峻的挑戰(zhàn),基于用戶的協(xié)同過濾推薦中的用戶興趣的定位問題和購買產(chǎn)品的風(fēng)險評估問題急待解決。本文對傳統(tǒng)的基于用戶的協(xié)同過濾算法改進(jìn),形成一套基于用戶興趣的可信購物推薦服務(wù)系統(tǒng)。該方法通過對用戶興趣元數(shù)據(jù)進(jìn)行分析形成用戶興趣分類,產(chǎn)生不同興趣類的數(shù)據(jù)集,通過對不同類的數(shù)據(jù)集分別進(jìn)行協(xié)同過濾算法訓(xùn)練,產(chǎn)生各個興趣類對應(yīng)的最優(yōu)模型,進(jìn)而對目標(biāo)用戶未評分的項目進(jìn)行預(yù)測,最后對預(yù)測推薦的項目采用信用風(fēng)險評判并過濾掉高風(fēng)險項目,實現(xiàn)推薦的項目是可信的。本文的主要工作有以下幾個方面:1)設(shè)計了基于用戶興趣的可信購物推薦服務(wù)系統(tǒng)架構(gòu),主要由數(shù)據(jù)預(yù)處理子系統(tǒng)、推薦子系統(tǒng)、交互控制系統(tǒng)、存儲子系統(tǒng)、數(shù)據(jù)后處理子系統(tǒng)、確認(rèn)子系統(tǒng)組成。2)設(shè)計了用戶興趣樹,用于從空間和時間維度上描述用戶興趣;實現(xiàn)了興趣樹的構(gòu)造和更新算法。3)對當(dāng)前協(xié)同過濾算法進(jìn)行適當(dāng)改進(jìn),設(shè)計并實現(xiàn)了推薦子系統(tǒng),提高了推薦商品的質(zhì)量和效率;根據(jù)推薦系統(tǒng)的輸入和輸出要素設(shè)計并實現(xiàn)了支撐用戶人機(jī)交互的交互控制子系統(tǒng)。4)設(shè)計并實現(xiàn)了基于興趣樹的推薦模型訓(xùn)練方法,可基于用戶興趣樹對用戶商品聚類,并通過協(xié)同過濾訓(xùn)練算法訓(xùn)練模型。5)綜合評估用戶商品購買風(fēng)險,設(shè)計并實現(xiàn)了風(fēng)險過濾方法。6)設(shè)計了推薦系統(tǒng)測試方案,并對系統(tǒng)進(jìn)行了驗證。測試數(shù)據(jù)來源為網(wǎng)上正常交易商品的數(shù)據(jù);測試結(jié)果顯示,本系統(tǒng)性能和推薦質(zhì)量相比較傳統(tǒng)推薦系統(tǒng)有所提升,并能過濾其中的風(fēng)險欺詐信息。
[Abstract]:With the development of the Internet, the variety and variety of online goods are becoming more and more diverse. Under the factors of multiple instability of online shopping, it is becoming more and more difficult for people to shop effectively on the Internet, and the efficiency of shopping will also decline. Restrict the development of electronic commerce of our country. With the rapid growth of the quantity of information and the types of goods on the Internet, the recommendation system is facing a severe challenge. It is urgent to solve the problem of the location of user interest and the risk assessment of purchasing products in the collaborative filtering recommendation based on users. In this paper, the traditional collaborative filtering algorithm based on users is improved to form a trusted shopping recommendation service system based on user interest. In this method, user interest classification is formed by analyzing user interest metadata, and the data sets of different interest classes are generated. By training the different classes of data sets with collaborative filtering algorithm, the optimal model of each interest class is generated. Finally, credit risk evaluation and filtering out high risk items are used to predict the items that are not graded by the target users, and the items that are recommended are credible. The main work of this paper is as follows: 1) the framework of trusted shopping recommendation service system based on user interest is designed, which consists of data preprocessing subsystem, recommendation subsystem, interactive control system, storage subsystem, etc. The user interest tree is designed to describe user interest in space and time dimension, and the interest tree construction and update algorithm .3) is implemented to improve the current collaborative filtering algorithm. The recommendation subsystem is designed and implemented to improve the quality and efficiency of the recommended goods. According to the input and output elements of the recommendation system, the interactive control subsystem. 4) which supports the user's human-computer interaction, is designed and implemented. The recommendation model training method based on the interest tree is designed and implemented. The user commodity can be clustered based on the user's interest tree. The collaborative filtering training algorithm training model. 5) is used to evaluate the risk of commodity purchase. The risk filtering method .6) is used to design and test the recommendation system, and the system is verified. The test results show that the performance and quality of the system are improved compared with the traditional recommendation system and can filter the information of risk fraud.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

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