本文選題：移動電子商務(wù)推薦 + 概率隱因子模型��； 參考：《江蘇大學(xué)》2017年碩士論文

【摘要】：隨著移動互聯(lián)網(wǎng)、智能終端技術(shù)的快速發(fā)展,具有移動性、便捷性的移動電子商務(wù)成為電子商務(wù)發(fā)展的新方向。受移動端顯示器大小的限制,移動電子商務(wù)面臨嚴(yán)重的信息過載現(xiàn)象,迫切需要為用戶提供個性化推薦服務(wù)�，F(xiàn)有的推薦技術(shù)主要利用用戶評分或者隱式反饋建立興趣模型,根據(jù)設(shè)定的時間間隔或者數(shù)據(jù)累積量定期更新該模型,為用戶提供個性化推薦。然而移動用戶的需求會隨著時間和位置的變化而發(fā)生改變,導(dǎo)致即時興趣變化迅速,現(xiàn)有算法的興趣模型主要反映用戶的長期興趣,需要對模型定期更新,不能滿足移動電商環(huán)境下為用戶提供實時推薦的要求。此外,由于新用戶沒有行為數(shù)據(jù)或歷史數(shù)據(jù)非常稀疏,現(xiàn)有算法無法為新用戶提供可靠的推薦結(jié)果,影響推薦質(zhì)量。針對上述問題,本文提出了基于用戶購買傾向和興趣度的個性化推薦算法和基于多源信息融合的協(xié)同過濾推薦算法。本文的主要研究工作如下:(1)針對移動電子商務(wù)中用戶的即時興趣變化迅速,而現(xiàn)有推薦算法不能實時響應(yīng)用戶需求的問題,提出了一種基于用戶購買傾向和興趣度的個性化推薦算法(Purchase Intention and Interest Degree,PIID)。該算法從長期興趣和即時興趣兩方面對用戶興趣建模,用購買傾向來量化用戶對最近交互過卻沒有購買的商品的即時興趣。在離線狀態(tài)下,提取用戶的行為特征,利用邏輯回歸模型訓(xùn)練影響購買的行為特征所對應(yīng)的回歸系數(shù),建立購買傾向預(yù)測模型;為了更精確地定位用戶可能購買的商品,將購買傾向與興趣度線性結(jié)合作為購買概率,針對購買記錄,利用概率隱因子模型通過最大化購買概率來學(xué)習(xí)用戶興趣度。在線推薦時,將用戶的實時行為特征帶入預(yù)測模型中,得到用戶的購買傾向,再與興趣度結(jié)合,將購買概率較大的商品列表推薦給用戶。在真實數(shù)據(jù)集上的實驗表明,PIID算法與現(xiàn)有算法相比,有較高的準(zhǔn)確率和F1指標(biāo),而且能夠提供高效的實時推薦。(2)針對現(xiàn)有推薦算法是在大量評分或隱式反饋的基礎(chǔ)上建立興趣模型,無法為缺乏行為數(shù)據(jù)的新用戶提供可靠推薦的問題,提出了一種基于多源信息融合的協(xié)同過濾推薦算法。算法首先利用位置信息,建立位置消費圖,計算位置遠近和不同用戶的興趣偏好對目標(biāo)用戶的影響,找到最近鄰居集,可以根據(jù)近鄰的偏好為新用戶進行推薦;接著利用PIID算法計算用戶的個人興趣;最后利用購買概率將近鄰偏好和個人興趣對購買的影響建模,為用戶推薦購買概率最大的商品列表。在真實數(shù)據(jù)集上的實驗表明,該算法有較高的準(zhǔn)確率和F1指標(biāo),而且能夠為新用戶提供可靠的推薦列表。(3)為了驗證本文所提出算法的可行性,采用面向?qū)ο蠛湍K化的設(shè)計思想,使用java編程語言設(shè)計并實現(xiàn)了一個基于用戶行為的移動電子商務(wù)原型推薦系統(tǒng)。
[Abstract]:With the rapid development of mobile Internet and intelligent terminal technology, mobile electronic commerce with mobility and convenience has become a new direction of electronic commerce. Limited by the size of mobile display, mobile e-commerce is faced with serious information overload phenomenon, so it is urgent to provide personalized recommendation services for users. The existing recommendation techniques mainly use user rating or implicit feedback to build interest model, update the model periodically according to the time interval or data accumulation, and provide personalized recommendation for users. However, the needs of mobile users will change with the change of time and location, which leads to the rapid change of immediate interest. The interest models of existing algorithms mainly reflect the long-term interests of users, and need to be updated regularly. Can not meet the mobile e-commerce environment for users to provide real-time recommendation requirements. In addition, due to the lack of behavioral data or sparse historical data for new users, the existing algorithms cannot provide reliable recommendation results for new users, which affects the quality of recommendation. To solve the above problems, this paper proposes a personalized recommendation algorithm based on user purchase tendency and interest degree and a collaborative filtering recommendation algorithm based on multi-source information fusion. The main research work of this paper is as follows: (1) aiming at the problem that the instant interest of users in mobile e-commerce changes rapidly, but the existing recommendation algorithms can not respond to the needs of users in real time. This paper presents a personalized recommendation algorithm based on user purchase tendency and interest degree. The algorithm models user interest in terms of both long-term interest and immediate interest, and quantifies users' immediate interest in recently interacted but not purchased items with purchasing tendency. In the off-line state, the user's behavior characteristics are extracted, and the regression coefficients corresponding to the behavior characteristics that affect the purchase are trained by using the logical regression model, and the prediction model of purchase tendency is established, in order to locate the goods that the user may buy more accurately. The linear combination of purchase propensity and interest degree is taken as the purchase probability. According to the purchase record, the probability implicit factor model is used to learn the user interest degree by maximizing the purchase probability. When online recommendation, the real-time behavior features of the user are brought into the prediction model, and then the user's purchase tendency is obtained, and then combined with the interest degree, the list of items with a high purchase probability is recommended to the user. Experiments on real data sets show that the PIID algorithm has higher accuracy and F1 index than the existing algorithms. Moreover, it can provide efficient real-time recommendation. (2) aiming at the problem that the existing recommendation algorithm is based on a large number of ratings or implicit feedback, it can not provide reliable recommendation for new users who lack behavioral data. A collaborative filtering recommendation algorithm based on multi-source information fusion is proposed. Firstly, the location consumption graph is built by using location information, and the influence of location distance and different user's interest preference on the target user is calculated, and the nearest neighbor set is found, and the new user can be recommended according to the nearest neighbor's preference. Then the PIID algorithm is used to calculate the personal interest of the user, and the influence of the nearest neighbor preference and personal interest on the purchase is modeled by using the purchase probability, and the list of items with the highest purchase probability is recommended for the user. Experiments on real data sets show that the proposed algorithm has high accuracy and F1 index, and can provide a reliable recommendation list for new users in order to verify the feasibility of the proposed algorithm. A prototype recommendation system for mobile e-commerce based on user behavior is designed and implemented by using object-oriented and modular design ideas and java programming language.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

【參考文獻】

相關(guān)期刊論文 前7條

1 李斌;張博;劉學(xué)軍;章瑋;;基于Jaccard相似度和位置行為的協(xié)同過濾推薦算法[J];計算機科學(xué);2016年12期

2 王智圣;李琪;汪靜;印鑒;;基于隱式用戶反饋數(shù)據(jù)流的實時個性化推薦[J];計算機學(xué)報;2016年01期

3 印鑒;王智圣;李琪;蘇偉杰;;基于大規(guī)模隱式反饋的個性化推薦[J];軟件學(xué)報;2014年09期

4 孫光福;吳樂;劉淇;朱琛;陳恩紅;;基于時序行為的協(xié)同過濾推薦算法[J];軟件學(xué)報;2013年11期

5 賈冬艷;張付志;;基于雙重鄰居選取策略的協(xié)同過濾推薦算法[J];計算機研究與發(fā)展;2013年05期

6 羅辛;歐陽元新;熊璋;袁滿;;通過相似度支持度優(yōu)化基于K近鄰的協(xié)同過濾算法[J];計算機學(xué)報;2010年08期

7 許海玲;吳瀟;李曉東;閻保平;;互聯(lián)網(wǎng)推薦系統(tǒng)比較研究[J];軟件學(xué)報;2009年02期

相關(guān)博士學(xué)位論文 前1條

1 鄧曉懿;移動電子商務(wù)個性化服務(wù)推薦方法研究[D];大連理工大學(xué);2012年

，

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