本文選題：反作弊 + 數(shù)據(jù)挖掘　； 參考：《哈爾濱工業(yè)大學》2016年碩士論文

【摘要】：隨著Internet和相關Web技術的發(fā)展,新的電子商務交易模式悄然興起。近年來,Online To Offline(O2O)模式飛速發(fā)展。O2O模式是一種將線下交易與互聯(lián)網(wǎng)結合在一起的新的商務模式,即線上網(wǎng)站通過提供打折、返利補貼、提供送貨服務等方式,把線下商店的消息推送給線上用戶,用戶在選定相關商戶之后在線下單、在線支付等流程,之后再憑借訂單去線下商家提取商品,或等待送貨上門的服務,或享受線下其他服務[1]。O2O市場中,各大公司為了搶占市場份額,紛紛推出了各種補貼機制來吸引用戶使用本公司產(chǎn)品。而對應的,“刷單”這一行業(yè)也是O2O公司所必須面對的問題。而目前刷單主要靠人工線下檢查為主,如監(jiān)控某個地區(qū)的單量異常,某家店鋪的消費均量異常等,然后進行線下檢查。作弊成本低,監(jiān)察成本高,是現(xiàn)在O2O公司所面臨的最大的問題。本課題基于某餐飲外賣O2O公司的反作弊部門,從打擊刷單的需求點出發(fā),提出了一種可以通過機器學習和數(shù)據(jù)挖掘相結合的方法,來檢測某一用戶為刷單用戶的風險,從而降低監(jiān)察的成本。本課題首先論證了O2O的作弊現(xiàn)象和網(wǎng)頁排名作弊現(xiàn)象的異同,并針對網(wǎng)頁排名的反作弊方法進行了修改使其契合本課題所面對的問題。同時,由于要對高風險用戶進行進一步操作,本文還開發(fā)了相應的后臺操作模塊,配合其他提高作弊成本的方式構成監(jiān)察系統(tǒng),從而降低刷單比例。數(shù)據(jù)平臺使用了敏捷開發(fā)的策略,同時使用了分布式數(shù)據(jù)庫等技術實現(xiàn)了自由維度組合生成報表的需求。目前,反作弊系統(tǒng)已經(jīng)上線五個月,并歷經(jīng)兩個版本的升級,數(shù)據(jù)表明,本文的方法可以有效的識別高風險用戶,并且提升了線下監(jiān)察部門的工作效率,有效降低了監(jiān)察成本。
[Abstract]:With the development of Internet and related Web technology, the new electronic commerce transaction mode rises quietly. In recent years, the online to offline (O2O) model has developed rapidly. The O2O model is a new business model that combines offline trading with the Internet, in which online websites offer discounts, rebate subsidies, delivery services, etc. Push the message from the offline store to the online user. After selecting the relevant merchant, the user sends out an order online, pays online, etc., and then relies on the order to pick up the goods from the offline merchant or to wait for the service to be delivered to the door-to-door. Or enjoy other services offline [1] .O2O market, in order to seize market share, companies have introduced a variety of subsidy mechanisms to attract users to use their products. And corresponding, "brush order" this industry also is the problem that O 2 O company must face. At present, the brushing order mainly depends on the manual line inspection, such as monitoring the single quantity anomaly in a certain area, the average consumption quantity of a shop, etc., and then carries on the offline inspection. Low cost of cheating and high cost of supervision are the biggest problems faced by O 2 O companies. Based on the anti-cheating department of a restaurant takeout O2O company, this paper presents a method that can be combined with machine learning and data mining to detect the risk of a certain user as a brushing user from the point of view of attacking the demand for brushing. This reduces the cost of monitoring. This paper first demonstrates the similarities and differences between the cheating phenomenon of O2O and the cheating phenomenon of the web page ranking, and modifies the anti-cheating method of the web page ranking so as to fit the problems faced by this subject. At the same time due to the high risk users to further operation this paper also developed the corresponding backstage operation module with other ways to increase the cost of cheating to form a monitoring system so as to reduce the proportion of brush orders. The data platform uses the strategy of agile development and the technology of distributed database to realize the requirement of generating report by free dimension combination. At present, the anti-cheating system has been on line for five months, and after two versions of the upgrade, data show that the method can effectively identify high-risk users, and improve the efficiency of offline supervision departments, effectively reduce the cost of supervision.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP311.52
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本文編號：2083515