本文選題：個性化推薦 + 用戶行為　； 參考：《哈爾濱商業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著電子商務(wù)和互聯(lián)網(wǎng)的發(fā)展普及,使得面向用戶的個性化推薦越來越被重視,準(zhǔn)確的推薦不僅僅能幫助用戶節(jié)省大量時間而且可以幫助電子商務(wù)網(wǎng)站贏得用戶的關(guān)注,進(jìn)而提高電子商務(wù)網(wǎng)站的銷量。然而不同用戶的背景不同,對推薦結(jié)果的期望也不同,因此需要從多個角度挖掘用戶隱藏的信息,更好的為用戶給出個性化的推薦。本文研究基于用戶的潛在行為以及社交關(guān)系發(fā)掘用戶的偏好,從以下方面優(yōu)化用戶個性化推薦:研究用戶行為時效特征以及用戶行為和偏好的關(guān)系,并提升預(yù)測用戶偏好的效率;研究網(wǎng)頁拓?fù)渑c網(wǎng)頁權(quán)重的關(guān)系,進(jìn)而優(yōu)化網(wǎng)頁排序,為用戶給出準(zhǔn)確的推薦;研究基于負(fù)向社交關(guān)系以及主題被推薦給用戶的概率特征為歷史信息非常稀少的非活躍用戶給出推薦,優(yōu)化基于負(fù)向社交關(guān)系和推薦主題概率特征的融合,為非活躍用戶給出準(zhǔn)確的推薦。提出以下方法優(yōu)化用戶的個性化推薦:(1)提出一種優(yōu)化基于行為感知的用戶個性化推薦方法,通過分析用戶的歷史訪問行為,建立映射用戶行為和偏好關(guān)系的隱馬爾可夫模型,并通過聚類減少優(yōu)化用戶參數(shù)的時間,得到一種平衡準(zhǔn)確度和時間復(fù)雜度的個性化推薦方法。(2)提出基于網(wǎng)頁拓?fù)渥R別網(wǎng)頁異常排名的用戶推薦優(yōu)化方法,基于網(wǎng)頁拓?fù)渥R別異常提升網(wǎng)頁排名的行為,研究網(wǎng)頁拓?fù)鋵W(wǎng)頁權(quán)重值的提升效果,并通過網(wǎng)頁的鏈入權(quán)重值和鏈入網(wǎng)頁數(shù)量的關(guān)系識別網(wǎng)頁異常排名現(xiàn)象,為個性化推薦創(chuàng)造公正的網(wǎng)頁排名環(huán)境,進(jìn)而提高對用戶的個性化推薦質(zhì)量。(3)提出基于負(fù)向社交關(guān)系和泊松過程融合的非活躍用戶推薦優(yōu)化方法,由用戶間的初始負(fù)向社交關(guān)系和衰減傳遞系數(shù)矩陣擴(kuò)展用戶間的負(fù)向社交關(guān)系,基于負(fù)向社交關(guān)系約束為非活躍用戶給出推薦主題的期望,然后基于泊松過程預(yù)測用戶對推薦主題滿意的概率,將概率高的推薦主題作為最終的推薦發(fā)送給用戶,為非活躍用戶給出準(zhǔn)確的推薦。
[Abstract]:With the development of electronic commerce and Internet, more and more attention has been paid to personalized recommendation for users. Accurate recommendation can not only help users save a lot of time, but also help e-commerce websites win the attention of users.And then improve the sales of e-commerce websites.However, different users have different backgrounds and different expectations for the recommended results. Therefore, it is necessary to mine hidden information from multiple angles to provide personalized recommendations for users.In this paper, we study the potential behaviors and social relationships of users to explore their preferences, and optimize their personalized recommendations from the following aspects: the characteristics of user behavior and the relationship between user behaviors and preferences.It also improves the efficiency of predicting user preferences, studies the relationship between web topology and web page weights, and then optimizes the ranking of web pages to give users accurate recommendations.The probabilistic features based on negative social relationships and topics recommended to users are proposed for inactive users with very little historical information, and the fusion of probability features based on negative social relationships and recommended themes is optimized.Give accurate recommendations for inactive users.This paper proposes the following methods to optimize the personalized recommendation of users: 1) A user personalized recommendation method based on behavioral awareness is proposed. By analyzing the historical access behavior of users, a hidden Markov model is established to map the relationship between user behavior and preference.Through clustering to reduce the time of optimizing user parameters, a personalized recommendation method, which balances accuracy and time complexity, is obtained.) based on the abnormal ranking of web page topology, a user recommendation optimization method is proposed.Based on the behavior of page topology recognition anomaly to enhance the ranking of web pages, this paper studies the effect of web topology on the weight of web pages, and identifies the abnormal ranking phenomenon of web pages by the relationship between the value of link weight and the number of web pages.In order to create a fair website ranking environment for personalized recommendation, and then improve the quality of personalized recommendation to users, this paper proposes an optimization method of inactive user recommendation based on negative social relationship and Poisson process fusion.The initial negative social relationship between users and the attenuation transfer coefficient matrix are used to expand the negative social relationship among users. Based on the constraint of negative social relationship, the expectation of recommending topics for inactive users is obtained.Then, based on the Poisson process, the probability of users' satisfaction with the recommendation topic is forecasted, and the recommendation topic with high probability is sent to the user as the final recommendation to give the accurate recommendation for inactive users.
【學(xué)位授予單位】：哈爾濱商業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3;F713.55

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