本文選題：微博　切入點(diǎn)：用戶(hù)興趣獲取　出處：《西北師范大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：微博簡(jiǎn)短、即時(shí)、便捷的特性使其很快吸引了大量的用戶(hù)群，并通過(guò)大量的轉(zhuǎn)發(fā)及評(píng)論裂變式的快速傳播與擴(kuò)散,產(chǎn)生大量信息流。為了從海量信息中找出用戶(hù)所感興趣的微博話題，就需要設(shè)計(jì)合理的興趣表示模型和準(zhǔn)確高效的微博用戶(hù)興趣推薦方法。其中，微博用戶(hù)興趣模型是實(shí)現(xiàn)微博用戶(hù)興趣推薦的基礎(chǔ),微博用戶(hù)興趣推薦是微博用戶(hù)興趣模型的應(yīng)用。因而，針對(duì)微博用戶(hù)興趣建模與推薦研究對(duì)于微博網(wǎng)站的發(fā)展十分重要。 本文以構(gòu)建微博用戶(hù)興趣模型及推薦方法作為研究背景，針對(duì)微博長(zhǎng)度短、信息量少，高維稀疏等特點(diǎn)，研究用戶(hù)興趣信息獲取、微博用戶(hù)興趣模型構(gòu)建及推薦方法，旨在構(gòu)建合理的微博用戶(hù)興趣模型及推薦方法，從而實(shí)現(xiàn)對(duì)微博用戶(hù)準(zhǔn)確高效的微博話題推薦。本文以新浪微博作為數(shù)據(jù)來(lái)源,主要做了如下工作： (1)分析微博中所包含的各種信息、微博用戶(hù)行為及其和微博用戶(hù)興趣間的關(guān)系,選擇合適的信息作為微博用戶(hù)興趣信息的來(lái)源，從而得到能夠準(zhǔn)確有效表示微博用戶(hù)興趣的信息。 (2)提出了一種基于詞項(xiàng)關(guān)聯(lián)關(guān)系與歸一化割加權(quán)非負(fù)矩陣分解的微博用戶(hù)興趣模型構(gòu)建方法。該方法首先基于詞分布上下文語(yǔ)義相關(guān)性來(lái)建立詞項(xiàng)關(guān)聯(lián)關(guān)系矩陣刻畫(huà)詞項(xiàng)間相似度，接著應(yīng)用歸一化割加權(quán)非負(fù)矩陣分解算法獲取用戶(hù)-主題矩陣，產(chǎn)生用戶(hù)感興趣的微博主題聚類(lèi)結(jié)果。實(shí)驗(yàn)表明，此方法能有效地進(jìn)行微博主題聚類(lèi)，并支持微博用戶(hù)興趣模型構(gòu)建。 (3)提出了一種基于用戶(hù)興趣模型與會(huì)話抽取算法的微博推薦方法。該方法首先應(yīng)用基于歸一化割加權(quán)非負(fù)矩陣分解的微博用戶(hù)興趣模型獲取用戶(hù)-主題矩陣，產(chǎn)生用戶(hù)感興趣的微博主題，然后結(jié)合基于Single-Pass聚類(lèi)模型的會(huì)話在線抽取算法SPFC(single-pass based on frequency and correlation)獲取微博的會(huì)話隊(duì)列并與用戶(hù)感興趣的微博主題進(jìn)行相似計(jì)算，最后等到實(shí)時(shí)的微博推薦結(jié)果。實(shí)驗(yàn)表明，此方法能有效地進(jìn)行微博推薦。 實(shí)驗(yàn)表明：本文提出的微博用戶(hù)興趣模型及推薦方法能夠有效地表征微博用戶(hù)的興趣并給出了相對(duì)準(zhǔn)確的推薦結(jié)果。
[Abstract]:Weibo's short, immediate and convenient features quickly attracted a large number of users, and spread and spread rapidly through a lot of retweeting and commenting on fission. In order to find out the topics of interest to users from the mass of information, we need to design a reasonable model of interest representation and an accurate and efficient recommended method of user interest. Weibo's user interest model is the basis of user interest recommendation for Weibo, and Weibo user interest recommendation is the application of user interest model. Therefore, the research on user interest modeling and recommendation is very important for the development of Weibo website. Based on the research background of constructing Weibo user interest model and recommending method, aiming at the characteristics of Weibo, such as short length, little information, high dimension sparse and so on, this paper studies the acquisition of user interest information and the building and recommendation method of user interest model. The purpose of this paper is to construct a reasonable user interest model and recommendation method for Weibo users, so as to realize the accurate and efficient topic recommendation for Weibo users. 1) analyzing the various information contained in Weibo, the user behavior of Weibo and the relationship between the user's interest and the user's interest, and selecting the appropriate information as the source of the user's interest information. Thus can accurately and effectively express Weibo user interest information. (2) A method of constructing Weibo user interest model based on word item association relation and normalized cut weighted nonnegative matrix decomposition is proposed. The method is based on the semantic relevance of word distribution context to establish the word item correlation matrix. Describe the similarity between words, Then the normalized cut weighted non-negative matrix decomposition algorithm is used to obtain the user-topic matrix, and the result of Weibo topic clustering of interest to the user is obtained. The experiment shows that this method can effectively carry out Weibo thematic clustering. And support Weibo user interest model construction. In this paper, we propose a Weibo recommendation method based on user interest model and session extraction algorithm. In this method, we first obtain the user-topic matrix by the Weibo user interest model, which is based on normalized cut weighted nonnegative matrix decomposition. The Weibo topic of interest to users is generated, and then the online session extraction algorithm based on Single-Pass clustering model, SPFC(single-pass based on frequency and correlation, is used to obtain the session queue of Weibo, and the similar computation is carried out with the topic Weibo of interest to the user. Finally, the real-time Weibo recommendation results. Experimental results show that this method can effectively recommend Weibo. The experimental results show that the proposed Weibo user interest model and the recommended method can effectively represent the user's interest and give a relatively accurate recommendation result.
【學(xué)位授予單位】：西北師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP393.092

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