發(fā)布時間：2019-03-27 09:17

【摘要】：隨著移動智能和互聯(lián)網(wǎng)的飛速發(fā)展，人們從信息匱乏的年代過度到了信息過載的時代。微博作為一種新型的社會化自媒體平臺，近年來用戶數(shù)量呈指數(shù)增長，每天生成大量的UGC（User Generating Content）。如何挖掘用戶的個人興趣建立用戶興趣模型，并將用戶感興趣的信息從海量信息中找出推薦給用戶顯得尤為重要。 本文以微博用戶的興趣建模和微博個性化信息推薦為研究內(nèi)容。主要包括： （1）傳統(tǒng)的向量空間模型和TF-IDF方法沒有考慮語義信息且存在用戶特征高維稀疏的問題，而常用的基于文檔級別詞共現(xiàn)的潛在狄利克雷分配模型（Latent Dirichletallocation,LDA）并不適用于微博這種短文本的主題挖掘和用戶興趣建模。鑒于此，本文引入適用于短文本的主題模型BTM（Biterm Topic Model）挖掘用戶的個人興趣，結合用戶興趣的多變性，提出基于時間窗口的用戶動態(tài)興趣模型。 （2）在用戶興趣模型的基礎上，，針對微博中用戶收聽列表信息過載的問題，提出綜合考慮微博本身質量、用戶個人興趣和社交興趣這三個主要特征的推薦模型，并在模型中引入?yún)f(xié)同過濾的思想。針對微博中用戶主動獲取的其他信息（非用戶收聽列表的信息），提出一種基于主題的信息推薦思想，并以美食主題為例，設計了整個應用。 （3）通過Big Data平臺獲取實驗數(shù)據(jù)，通過實驗驗證了BTM建立的用戶興趣模型在推薦性能上要優(yōu)于LDA及TF-IDF模型且考慮用戶興趣的多變性能進一步優(yōu)化推薦效果；在三個主要影響因素中，結合了協(xié)同過濾思想的用戶個人興趣特征推薦性能最優(yōu)，用戶社交興趣特征次之，微博本身質量特征最差； 本文提出的推薦模型從用戶興趣建模出發(fā)，針對不同的場景結合不同的特征構建推薦模型，任何UGC平臺的信息推薦問題都能夠在本文的研究基礎上進行擴展利用。
[Abstract]:With the rapid development of mobile intelligence and Internet, people from the era of lack of information to the era of information overload. Weibo, as a new type of social self-media platform, has seen an exponential increase in the number of users in recent years, generating a large number of UGC (User Generating Content). Every day. How to mine the user's personal interest to establish the user interest model, and find out the information that the user is interested in from the massive information to recommend to the user is very important. This article takes Weibo user's interest modeling and Weibo personalized information recommendation as the research content. The main contents are as follows: (1) the traditional vector space model and TF-IDF method do not consider semantic information and have the problem of high-dimensional sparse user characteristics. However, the commonly used latent Dirichlet allocation model based on document-level co-occurrence of words (Latent Dirichletallocation, LDA) is not suitable for topic mining and user interest modeling of short text such as Weibo. In view of this, this paper introduces a topic model, BTM (Biterm Topic Model), which is suitable for short text, to mine users' personal interests. Combined with the variability of user's interests, a dynamic user interest model based on time window is proposed in this paper. (2) on the basis of user interest model, aiming at the problem of information overload in Weibo's listening list, a recommendation model considering Weibo's own quality, user's personal interest and social interest is put forward. The idea of collaborative filtering is introduced into the model. Aiming at the other information (non-user listens list information) obtained by users in Weibo, this paper puts forward a subject-based information recommendation idea, and designs the whole application with the gourmet theme as an example. (3) get the experimental data through the Big Data platform, and verify that the user interest model established by BTM is better than the LDA and TF-IDF model in recommendation performance, and further optimizes the recommendation effect considering the changeable performance of user interest. Among the three main influencing factors, the recommended performance of user's personal interest characteristics combined with collaborative filtering is the best, that of user's social interest is the second, and Weibo's own quality is the worst. The recommendation model proposed in this paper starts from the user interest modeling and constructs the recommendation model according to different scenarios combined with different features. Any information recommendation problem on UGC platform can be extended and utilized on the basis of the research in this paper.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP391.3;TP393.092

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本文編號：2448060