本文選題：電商用戶畫像 + 行為序列�。� 參考：《中國科學技術大學》2017年碩士論文

【摘要】：近年來,電子商務的飛速發(fā)展給人們的生活帶來了巨大便利,人們越來越傾向于從電商網(wǎng)站中直接在線獲取所需的商品和服務。在促使用戶改變消費習慣的同時,電商網(wǎng)站也在后臺記錄了海量用戶行為日志。為提升用戶體驗,適宜地推薦商品,這些日志信息已經(jīng)被廣泛應用于電商網(wǎng)站的畫像和推薦系統(tǒng)。為了構建完善有效的畫像和推薦系統(tǒng),首先需要對用戶未來的消費情況做出預測。目前電商平臺大都以統(tǒng)計和建模歷史消費信息的方式達成該目標,而較少考慮用戶歷史行為序列中包含的比較和選擇信息。這種方式在商品數(shù)量日益繁多的今天已不再適用。因此本文提出了一種基于選擇模型的方法,將用戶歷史消費情況與行為序列特征相結合,以更好地表達用戶偏好,預測其消費情況。更進一步,用戶消費時的行為表現(xiàn)由其心理狀態(tài)決定,研究用戶消費心理將會有助于理解用戶需求、提供多元智能化服務。基于調(diào)查問卷的傳統(tǒng)用戶消費心理研究方法不僅耗時耗力而且具有很強的主觀性。而大數(shù)據(jù)時代的到來,使利用歷史行為數(shù)據(jù)建模用戶心理逐漸成為可能。基于此,本文對用戶消費時的猶豫心理進行了研究,提出了一種數(shù)據(jù)驅動的猶豫心理建模方法�？偨Y來看,本文的主要研究內(nèi)容和貢獻如下:1)對基于選擇模型的用戶消費預測方法進行了研究。本文首先引入機會成本的概念并使用一個序列效用函數(shù)預估用戶每個Session中的最佳替代品;接下來在每個Session中的被購買商品和最佳替代品之間建立基于潛在因子的選擇模型;更進一步,利用Session中的所有比較信息提出了將潛在因子和行為序列效用結合的選擇模型;最后,使用天貓網(wǎng)站的真實數(shù)據(jù)集驗證了提出算法的有效性。2)對數(shù)據(jù)驅動的用戶猶豫心理建模和應用方法進行了研究。本文首先根據(jù)用戶行為序列表現(xiàn)出的幾種特征定義和計算每個Session的可觀測猶豫指數(shù)Ds;然后考慮到Ds受用戶和商品的共同影響,構建了用戶-商品的猶豫矩陣分解模型將Ds分解為用戶和商品的猶豫因子;接下來,探討了幾種利用用戶猶豫心理建模的結果提供多元智能服務的方法;最后,同樣在天貓網(wǎng)站的真實數(shù)據(jù)集上對提出的方法進行了驗證。
[Abstract]:In recent years, the rapid development of electronic commerce has brought great convenience to people's life. People are more and more inclined to obtain the needed goods and services directly from e-commerce websites. While urging users to change their consumption habits, e-commerce websites also record massive user behavior logs in the background. In order to enhance the user experience and recommend products appropriately, the log information has been widely used in the portrait and recommendation system of e-commerce websites. In order to construct an effective portrait and recommendation system, it is necessary to predict the future consumption of users. At present, most e-commerce platforms achieve this goal by means of statistics and modeling of historical consumption information, while less consideration is given to the comparison and selection information contained in the historical behavior sequence of users. This approach is no longer applicable at a time when there are more and more goods. Therefore, this paper proposes a method based on selection model, which combines the historical consumption of users with the characteristics of behavior sequence to better express user preferences and predict their consumption. Furthermore, the behavior of users is determined by their psychological state. The study of consumer psychology will help to understand the needs of users and provide multiple intelligent services. The traditional research method of consumer psychology based on questionnaire is not only time-consuming and labor-intensive, but also highly subjective. With the advent of big data, it is possible to use historical behavior data to model user psychology. Based on this, this paper studies the hesitancy psychology of user consumption, and proposes a data-driven modeling method of hesitant psychology. In conclusion, the main contents and contributions of this paper are as follows: 1) the user consumption prediction method based on selection model is studied. This paper first introduces the concept of opportunity cost and uses a sequential utility function to estimate the best alternatives in each Session, and then establishes a selection model based on potential factors between the purchased goods and the best alternatives in each Session. Furthermore, using all the comparative information in Session, a selection model combining potential factor and behavioral sequence utility is proposed. The validity of the proposed algorithm is verified by the real data set of Tmall website. 2) the modeling and application methods of data-driven user hesitancy are studied. In this paper, we first define and calculate the observable hesitation index (DS) of each Session based on several characteristics of user behavior sequence, and then consider that Ds are influenced by both user and commodity. The user-commodity hesitation matrix decomposition model is constructed to decompose Ds into user and commodity hesitation factors. Then, several methods to provide multiple intelligent services based on the results of user hesitation modeling are discussed. The proposed method is also validated on the real data set of Tmall website.
【學位授予單位】：中國科學技術大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F713.55;F713.36

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本文編號：1927904