本文關(guān)鍵詞：基于用戶分解和社交融合的推薦算法研究　出處：《華東師范大學(xué)》2016年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 推薦系統(tǒng) 用戶分解 情景感知推薦 社交推薦 非對(duì)稱社交影響力

【摘要】：在互聯(lián)網(wǎng)時(shí)代,人們不受時(shí)空限制地享受著互聯(lián)網(wǎng)提供的信息和服務(wù)的同時(shí),也不得不面對(duì)海量且規(guī)模不斷增長(zhǎng)的數(shù)據(jù)以及大量的無效信息。推薦系統(tǒng)作為信息過濾的重要工具,同時(shí)為用戶提供個(gè)性化的信息服務(wù),越來越受到人們的歡迎,并且必不可少。典型的推薦技術(shù)為協(xié)同過濾算法,該算法利用其他購買過共同物品的相似用戶來預(yù)測(cè)當(dāng)前用戶的偏好,由于其重要的應(yīng)用價(jià)值和學(xué)術(shù)價(jià)值,已經(jīng)被工業(yè)界和學(xué)術(shù)界廣泛的研究。然而,在多個(gè)用戶的歷史行為混合且無法顯式區(qū)分時(shí),或用戶行為受到系統(tǒng)中的其他用戶影響時(shí),很多算法都不能較好地準(zhǔn)確表示用戶的真實(shí)興趣或偏好,進(jìn)而影響到推薦服務(wù)的性能。因此,本文嘗試用虛擬用戶重新表示每個(gè)用戶的真實(shí)偏好,并提高推薦的品質(zhì)。虛擬用戶是表示真實(shí)用戶興趣并用于幫助產(chǎn)生推薦的向量或配置文件。針對(duì)以上兩種情況,本文分別從用戶分解和社交融合兩個(gè)方面研究虛擬用戶對(duì)推薦性能的影響。本文首先提出兩種用戶分解算法來研究用戶歷史行為混合的情況下,如何表示虛擬用戶,利用虛擬用戶識(shí)別真實(shí)用戶,并為識(shí)別的用戶做個(gè)性化推薦。在網(wǎng)絡(luò)協(xié)議電視服務(wù)中,家庭成員無差別地使用該服務(wù),為研究混合的用戶行為提供良好的研究對(duì)象,這里的兩種方法都是在該服務(wù)上進(jìn)行。第一種方法是基于時(shí)間片分割的用戶識(shí)別推薦算法。該方法定義一個(gè)時(shí)間片內(nèi)的活動(dòng)為一個(gè)虛擬用戶的活動(dòng),并利用隱式評(píng)分捕獲虛擬用的偏好,合并偏好相似的虛擬用戶作為真實(shí)用戶,并為這些用戶做個(gè)性化的推薦；第二種方法是基于子空間聚類的用戶識(shí)別推薦算法。該算法基于賬戶-項(xiàng)目-時(shí)間的張量分解和子空間聚類來發(fā)現(xiàn)用戶的時(shí)序行為,利用聚類的時(shí)間段來表示虛擬用戶,合并偏好相似的虛擬用戶作為真是用戶,并為這些用戶做個(gè)性化的推薦。實(shí)驗(yàn)表明兩種方法都比之前的方法性能更優(yōu),并且第二種方法比第一種方法更自動(dòng)化且性能更好。在社交網(wǎng)絡(luò)中,用戶之間進(jìn)行各種各樣的交流,為研究用戶間的興趣相互影響提供了理想的研究對(duì)象。因此,基于用戶(或用戶對(duì)物品的偏好)受到其社交鄰居的影響這一假設(shè),本文進(jìn)一步提出融合社交影響力的兩種新穎的推薦算法。這兩種方法在用戶-物品矩陣分解框架的基礎(chǔ)上融合用戶-用戶社交鏈接矩陣。具體而言,利用用戶評(píng)分和該用戶社交鄰居的評(píng)分構(gòu)建虛擬用戶來表示用戶的真實(shí)偏好。第一種融合個(gè)性化因素和加權(quán)社交影響力的方法,利用用戶和社交鄰居之間的隱式偏好構(gòu)建社交影響力,在預(yù)測(cè)時(shí)同時(shí)結(jié)合用戶個(gè)人因素和社交影響力帶來的偏好影響,在很大程度上可以提高對(duì)評(píng)分較少的用戶的預(yù)測(cè)性能。然而,每個(gè)用戶受到其社交鄰居的影響程度不同：不僅個(gè)數(shù)不同,而且相互的影響程度也不同。因此,第二種方法利用非對(duì)稱社交影響力來重新表示用戶間產(chǎn)生的相互影響。同時(shí),實(shí)驗(yàn)結(jié)果證明本文提出的方法比目前的其他方法更加準(zhǔn)確而高效。
[Abstract]:In the Internet era, people enjoy the information and services provided by the Internet without being limited by time and space. At the same time, they also have to face massive and growing data and lots of invalid information. As an important tool for information filtering and providing personalized information services for users, the recommendation system is becoming more and more popular and indispensable. The typical recommendation technology is collaborative filtering algorithm, which uses other similar users who purchase common goods to predict the current user preferences. Because of its important application value and academic value, it has been widely studied by industry and academia. However, when many users' historical behaviors are mixed and cannot be distinguished distinctions, or users' behaviors are influenced by other users in the system, many algorithms can not accurately represent the users' real interests or preferences, and further affect the performance of recommendation services. Therefore, this article attempts to reexpress the true preferences of each user with a virtual user and improve the quality of the recommendation. A virtual user is a vector or configuration file that represents a real user's interest and is used to help produce a recommendation. In view of the above two situations, this paper studies the effect of virtual users on the performance of recommendation from two aspects of user decomposition and social integration. In this paper, two user decomposition algorithms are first proposed to study how to represent virtual users under the condition of mixed user behavior, to identify the real users by virtual users, and to make personalized recommendation for the identified users. In the network protocol TV service, family members use this service without distinction, providing good research objects for studying the mixed user behavior. The two methods here are carried out on the service. The first method is a user recognition recommendation algorithm based on time slice segmentation. This method defines a time slice of the activity as a virtual user activity, and the implicit score for virtual capture preferences, preferences with similar virtual users as real users, and these users make personalized recommendation; the second method is user identification subspace clustering recommendation algorithm based on. Based on account item time tensor decomposition and subspace clustering, the algorithm finds user's temporal behavior, uses clustering time to express virtual users, and combines virtual users with similar preferences as real users, and makes personalized recommendation for these users. Experiments show that the two methods are better than the previous ones, and the second methods are more automated and better than the first one. In social networks, a variety of communication between users provides an ideal research object for the study of the interaction between users. Therefore, based on the hypothesis that users (or users' preferences for goods) are influenced by their social neighbors, this paper further proposes two novel recommendation algorithms that integrate social influence. These two methods integrate the user - user social link matrix on the basis of the user - object matrix decomposition framework. In particular, the user's real preference is expressed by building a virtual user by using the user score and the score of the user's social neighbor. The first method and the weighted fusion individual factors influence social construction, social influence the implicit preference between the user and the social neighbors, when combined with the impact of the user's personal factors and social influence preferences, to a large extent can improve the prediction performance of the score less user. However, each user has a different degree of influence by its social neighbors: not only a different number, but also a different degree of influence. Therefore, the second methods use asymmetric social influence to rerepresent the interaction between users. At the same time, the experimental results show that the proposed method is more accurate and efficient than the other methods.
【學(xué)位授予單位】：華東師范大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.3

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