基于微博用戶興趣模型的個性化廣告推薦研究
發(fā)布時間：2019-06-17 12:34
【摘要】：隨著互聯(lián)網(wǎng)技術以及信息傳播技術的飛速發(fā)展,基于web2.0平臺的微博等開放互聯(lián)網(wǎng)社交服務模式越來越流行。在微博平臺中,人人都像媒體一樣可以自由發(fā)表感受和見解。近年來,基于微博的數(shù)據(jù)挖掘相關研究越來越多,本文通過構建微博用戶興趣模型,針對用戶在微博平臺發(fā)布的海量數(shù)據(jù),挖掘能揭示用戶興趣點的關鍵主題詞,并根據(jù)挖掘結果進一步深入探討了如何實現(xiàn)個性化的廣告推薦,從而幫助廣告主們降低廣告成本,提升廣告的投放效果。 本文對如何利用微博數(shù)據(jù)對用戶興趣進行分析,以及實現(xiàn)個性化廣告推薦的方法和形式進行了研究和探索。與該領域已有的研究工作相比,本文主要有以下幾點不同： 首先,對不同的主題模型進行分析,比較了TwitterRank、Author-Topic和TwitterLDA三種主題模型在構建微博用戶興趣模型方面的性能,結合本文的研究內(nèi)容,選擇采用TwitterLDA模型進行新浪微博用戶的興趣識別。 其次,將目前已有的改進后的LDA算法應用于微博用戶主題詞的挖掘,通過分析主題結構(topic structure)里的后驗概率,來找出了能夠表達主題含義的短語。改進后的算法既能保留傳統(tǒng)LDA模型調(diào)換詞序對主題挖掘結果沒有影響的特點,同時還能使算法變得更高效,并獲得了能表示主題含義的n-gram短語。 最后,提出在微博個性化廣告推薦的各種廣告形式中融合故事型廣告的創(chuàng)新模式并設計了以新浪微博普通用戶為例的實證調(diào)研。最終通過對調(diào)研結果進行分析,驗證了論文中使用的主題模型在普通微博用戶中進行興趣挖掘的可行性及有效性,并簡單地就故事型廣告的創(chuàng)新形式接納度和興趣模型的有效性進行了調(diào)研評估。 通過本文的研究,可以發(fā)現(xiàn),微博用戶的行為和興趣之間有很強的關聯(lián)性,尤其是發(fā)布行為、轉發(fā)行為和評論行為這三種主要行為�；谖⒉┯脩襞d趣模型的個性化廣告推薦研究能夠分析微博用戶的興趣并進行精準的廣告投放,降低廣告成本,提高廣告收益,帶來更好的經(jīng)濟及社會效益。
[Abstract]:With the rapid development of Internet technology and information communication technology, open Internet social service models such as Weibo based on web2.0 platform are becoming more and more popular. In the Weibo platform, everyone is as free to express their feelings and opinions as the media. In recent years, there are more and more research on data mining based on Weibo. This paper constructs Weibo user interest model, mining the key subject words that can reveal the points of interest of users, and further discusses how to realize personalized advertising recommendation according to the mining results, so as to help advertisers reduce the cost of advertising and improve the effect of advertising. This paper studies and explores how to use Weibo data to analyze user interest and how to realize personalized advertising recommendation. Compared with the existing research work in this field, this paper mainly has the following differences: firstly, the different topic models are analyzed, and the performance of TwitterRank,Author-Topic and TwitterLDA in building Weibo user interest model is compared. combined with the research content of this paper, the TwitterLDA model is selected to identify the interest of Sina Weibo users. Secondly, the improved LDA algorithm is applied to the mining of topic words of Weibo users. By analyzing the posterior probability in the topic structure (topic structure), the phrases that can express the meaning of the topic are found out. The improved algorithm can not only preserve the characteristic that the traditional LDA model changing word order has no effect on the topic mining results, but also make the algorithm more efficient, and obtain the n-gram phrase which can express the meaning of the topic. Finally, this paper puts forward the innovative mode of integrating story-based advertising into various advertising forms recommended by Weibo personalized advertising, and designs an empirical investigation with Sina Weibo ordinary users as an example. Finally, through the analysis of the research results, the feasibility and effectiveness of the topic model used in this paper in interest mining among ordinary Weibo users are verified, and the innovative form acceptance of story advertising and the effectiveness of interest model are simply investigated and evaluated. Through the study of this paper, we can find that there is a strong correlation between the behavior and interest of Weibo users, especially the three main behaviors: publishing behavior, forwarding behavior and comment behavior. The personalized advertising recommendation research based on Weibo user interest model can analyze the interests of Weibo users and carry out accurate advertising, reduce advertising costs, improve advertising revenue, and bring better economic and social benefits.
【學位授予單位】：華東師范大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：G358;F713.8
本文編號：2500992
[Abstract]:With the rapid development of Internet technology and information communication technology, open Internet social service models such as Weibo based on web2.0 platform are becoming more and more popular. In the Weibo platform, everyone is as free to express their feelings and opinions as the media. In recent years, there are more and more research on data mining based on Weibo. This paper constructs Weibo user interest model, mining the key subject words that can reveal the points of interest of users, and further discusses how to realize personalized advertising recommendation according to the mining results, so as to help advertisers reduce the cost of advertising and improve the effect of advertising. This paper studies and explores how to use Weibo data to analyze user interest and how to realize personalized advertising recommendation. Compared with the existing research work in this field, this paper mainly has the following differences: firstly, the different topic models are analyzed, and the performance of TwitterRank,Author-Topic and TwitterLDA in building Weibo user interest model is compared. combined with the research content of this paper, the TwitterLDA model is selected to identify the interest of Sina Weibo users. Secondly, the improved LDA algorithm is applied to the mining of topic words of Weibo users. By analyzing the posterior probability in the topic structure (topic structure), the phrases that can express the meaning of the topic are found out. The improved algorithm can not only preserve the characteristic that the traditional LDA model changing word order has no effect on the topic mining results, but also make the algorithm more efficient, and obtain the n-gram phrase which can express the meaning of the topic. Finally, this paper puts forward the innovative mode of integrating story-based advertising into various advertising forms recommended by Weibo personalized advertising, and designs an empirical investigation with Sina Weibo ordinary users as an example. Finally, through the analysis of the research results, the feasibility and effectiveness of the topic model used in this paper in interest mining among ordinary Weibo users are verified, and the innovative form acceptance of story advertising and the effectiveness of interest model are simply investigated and evaluated. Through the study of this paper, we can find that there is a strong correlation between the behavior and interest of Weibo users, especially the three main behaviors: publishing behavior, forwarding behavior and comment behavior. The personalized advertising recommendation research based on Weibo user interest model can analyze the interests of Weibo users and carry out accurate advertising, reduce advertising costs, improve advertising revenue, and bring better economic and social benefits.
【學位授予單位】：華東師范大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：G358;F713.8
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