本文選題：上下文感知交互　切入點(diǎn)：推薦系統(tǒng)　出處：《山東師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：伴隨著數(shù)字化網(wǎng)絡(luò)信息的普及,網(wǎng)絡(luò)上的信息承載越來越大。推薦系統(tǒng)的作用就是讓用戶在海量的數(shù)據(jù)中快速得到自己需要的信息。各種各樣的推薦系統(tǒng)也就應(yīng)運(yùn)而生,然而現(xiàn)在大多數(shù)針對(duì)推薦算法的研究都是關(guān)注提高推薦算法的精確度。然而我們熟悉的情境是,不同的用戶的需求是不同的,現(xiàn)在的人們置身于不同的社會(huì)事務(wù)當(dāng)中,每個(gè)人在不同的時(shí)間節(jié)點(diǎn)、地點(diǎn)、情境等會(huì)有不同的偏好。本文認(rèn)為不同的用戶所處的上下文信息不同,并且用戶所處的上下文情境對(duì)推薦系統(tǒng)不可見的,這就要求我們的推薦系統(tǒng)去探知用戶所處的上下文情境,并能夠按照探知的上下文情境進(jìn)行實(shí)時(shí)的推薦,但是現(xiàn)在的上下文推薦系統(tǒng)對(duì)于這種感知功能的關(guān)注度還是比較低的,像是感知交互的過程等,很多問題還需要做出深一步的研究。本文對(duì)上下文感知交互推薦進(jìn)行的主要研究工作如下:1.提出一種上下文感知的交互推薦算法。針對(duì)實(shí)時(shí)的推薦系統(tǒng),用戶的上下文情境是處于多變的情況下的,很難直接用傳統(tǒng)的用戶偏好提取技術(shù)訓(xùn)練用戶的偏好模型。并且針對(duì)這種上下文情境多變的情況,用戶的上下文信息對(duì)系統(tǒng)是完全不可見的。本文認(rèn)為針對(duì)用戶上下文情境的探知,可以運(yùn)用系統(tǒng)的推薦列表與用戶對(duì)系統(tǒng)推薦的反饋列表進(jìn)行交互的模式進(jìn)行探知,本文提出一種交互模式,能夠動(dòng)態(tài)的探知上下文的改變,并在此過程中實(shí)時(shí)更新用戶偏好模型,并用這種偏好模型監(jiān)視可能出現(xiàn)的探知偏差。本文提出對(duì)于推薦列表的更新是一種動(dòng)態(tài)隨機(jī)分配最優(yōu)化問題,本文提出TP-Learning算法,這個(gè)算法是對(duì)貪婪算法的一種改進(jìn),是基于啟發(fā)式學(xué)習(xí)的,能夠?yàn)橛脩舻玫揭粋�(gè)效能最大化的推薦列表。本文提出的算法就是能夠?qū)崟r(shí)的去發(fā)現(xiàn)用戶上下文的改變,并按照當(dāng)前的上下文模式對(duì)用戶偏好進(jìn)行實(shí)時(shí)更新,并且運(yùn)用這種機(jī)制提高推薦算法的表現(xiàn)。2.改進(jìn)網(wǎng)絡(luò)結(jié)構(gòu)推薦算法(NBI)提出基于時(shí)間衰減和用戶相似權(quán)重的二部圖推薦算法(TUserCF)�；诙繄D的推薦算法是將每個(gè)用戶節(jié)點(diǎn)被賦予的資源值均分給相鄰的節(jié)點(diǎn),本文認(rèn)為應(yīng)該將用戶選擇物品的時(shí)間因素考慮進(jìn)去;我們對(duì)于算法的改進(jìn)是基于當(dāng)前社交網(wǎng)絡(luò)中社會(huì)化的因素,將用戶之間的關(guān)系進(jìn)行擴(kuò)充。我們對(duì)于資源值的分配問題時(shí),首先考慮的就是評(píng)分矩陣的影響,其次就是用戶與用戶之間的興趣集合問題,好友集合參數(shù),并且對(duì)于時(shí)間的衰減進(jìn)行說明,并基于時(shí)間的衰減引入了分配系數(shù)的加權(quán)改變過程。最后按照最后的資源值進(jìn)行推薦,但是這種資源值的推薦也會(huì)將評(píng)分的因素考慮在內(nèi)。我們的算法顯著提高了被推薦物品的準(zhǔn)確性,能夠使推薦更有效率,因此有很強(qiáng)的應(yīng)用價(jià)值。3.本文設(shè)計(jì)了融合上下文信息的電子商務(wù)推薦系統(tǒng)框架,它是綜合了用戶具體屬性、用戶行為以及第三章中我們提出的上下文信息屬性,構(gòu)造N層笛卡爾積的屬性集合,運(yùn)用邏輯回歸理論構(gòu)建了融合上下文屬性特征的電子商務(wù)網(wǎng)站推薦系統(tǒng)架構(gòu)。這個(gè)架構(gòu)是對(duì)全文推薦系統(tǒng)結(jié)構(gòu)的一種總結(jié),是綜合了電子商務(wù)網(wǎng)站特點(diǎn)與推薦系統(tǒng)特點(diǎn)的綜合產(chǎn)物。
[Abstract]:Along with the popularization of digital network information, network information carrying more and more. The recommendation system function is to allow users to quickly get the information they need in the vast amounts of data. All kinds of recommendation system also arises at the historic moment, but now most of the studies aimed at the recommended algorithm is the focus of the improved recommendation algorithm accuracy. However we are familiar with the situation is different, the user's needs are different, now people are living in a different social affairs, each place in the time node, different situation, will have different preferences. The context information of different users at different context and user's the recommendation system is not visible, this requires our recommendation system to ascertain the user's context, and to ascertain the real context according to the push Recommended, but attention to context recommender systems for this sensing function is still relatively low, as is the interactive process, many problems still need to make further research. Based on the context aware interaction recommended the main research work was as follows: 1. put forward a kind of interactive context aware recommendation algorithm. According to the real-time recommendation system, user context is in the changing circumstances, it is difficult to directly extract the user preference model for technical training. And the traditional user preferences in this context changing situation, the context information is completely invisible to the system. The discovery for the user context. And can use the recommendation list and user system interaction on the list of recommended feedback system model for discovery, this paper proposes a kind of interactive mode, can Dynamic discovery context changes, and updates the user preferences in the process model, and use this preference model to monitor possible deviation detection. In this paper, the recommendation list update is a dynamic random allocation optimization problem, this paper proposes a TP-Learning algorithm, this algorithm is a modified greedy algorithm, which is based on heuristic learning, can get a list of recommended maximum efficiency for users. This algorithm is capable of real-time to find user context changes, and real-time update of user preferences in accordance with the current context model, and the use of this mechanism to improve the performance of the.2. recommendation algorithm improved network structure recommendation algorithm (NBI) is proposed two similar weight decay time and user based recommendation algorithm (TUserCF). Two recommendation algorithm based on graph is each user node The point was given the resources value to adjacent nodes, we should take into account the goods time factors for our users; the algorithm is the social network of factors based on the relationship between users. We extend the problem of resources allocation for value, the first consideration is the scoring matrix the second is the impact between the user and the user's interest set, friends set parameters, and for the time attenuation is described, and the weighted change process distribution coefficient is introduced based on the time attenuation. Finally, according to the last resource values are recommended, but the resources of the recommended value will also score into account. Our algorithm significantly improves the accuracy of recommended items, can make the recommendation more efficiency, so it has strong application value of.3. is designed in this paper on the integration The electronic commerce recommendation system framework, it is a combination of a specific user attributes, context information of user behavior and attributes in the third chapter, we put forward the attribute structure of N layer of Cartesian product of a set of regression theory to construct the e-commerce website integration context attributes recommendation system architecture application logic. This architecture is a summary of recommendations the system structure of the whole thesis, is the integrative product of e-commerce website features and characteristics of the recommendation system.

【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

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