【摘要】：隨著網(wǎng)絡(luò)信息的增加,信息過載現(xiàn)象日益嚴(yán)重。針對微博平臺中的信息過載和推薦準(zhǔn)確率低的問題,本文利用社交網(wǎng)絡(luò)、奇異值分解、文本分類等技術(shù)對微博推薦效果進(jìn)行改進(jìn)。提出并介紹了三種推薦算法,并對三種推薦算法進(jìn)行了深入的研究。 本文的主要研究內(nèi)容如下： (1)研究了基于社交網(wǎng)絡(luò)的推薦算法,并在此基礎(chǔ)上提出了基于社交網(wǎng)絡(luò)和信任度的微博好友推薦算法。該算法針對社交網(wǎng)絡(luò)推薦準(zhǔn)確率低的問題,提出通過結(jié)合用戶關(guān)注關(guān)系和用戶行為來預(yù)測用戶偏好的方法,并在此基礎(chǔ)上為用戶做好友推薦。 (2)針對微博中沒有評分?jǐn)?shù)據(jù)的問題,本文通過將用戶行為轉(zhuǎn)化為評分的方法進(jìn)行用戶偏好預(yù)測,并提出了基于奇異值分解的微博好友推薦算法,該算法通過對生成的用戶評分矩陣進(jìn)行奇異值分解來降低矩陣維數(shù),并結(jié)合用戶相似性和文本相似性進(jìn)行偏好預(yù)測和推薦。 (3)由于微博中含有大量的文本,并且用戶喜好的種類不唯一,因此本文提出了基于文本分類的微博內(nèi)容推薦算法。該算法針對微博平臺中存在的大量文本信息進(jìn)行文本挖掘,通過文本分類對用戶的偏好進(jìn)行整理,分類的為用戶進(jìn)行內(nèi)容推薦,從而提高推薦的準(zhǔn)確率和召回率。 最后通過實驗測試表明三種推薦算法在準(zhǔn)確率和召回率方面有了一定程度的提高。
[Abstract]:With the increase of network information, the phenomenon of information overload is becoming more and more serious. Aiming at the problem of information overload and low recommendation accuracy in Weibo platform, this paper uses social network, singular value decomposition, text classification and other techniques to improve the recommended effect of Weibo. Three recommendation algorithms are proposed and introduced, and three recommendation algorithms are studied in depth. The main contents of this paper are as follows: (1) the recommendation algorithm based on social network is studied, and on this basis, Weibo friend recommendation algorithm based on social network and trust degree is proposed. Aiming at the problem of low recommendation accuracy in social networks, this algorithm proposes a method to predict user preferences by combining user concern and user behavior. And on this basis to make friends recommendation for users. (2) in order to solve the problem that Weibo has no scoring data, this paper uses the method of converting user behavior to scoring to predict user preference. A friend recommendation algorithm of Weibo based on singular value decomposition (SVD) is proposed. The algorithm reduces the dimension of the generated user score matrix by singular value decomposition. And combined with user similarity and text similarity to predict and recommend preferences. (3) because Weibo contains a large number of texts, and user preferences are not unique, Therefore, this paper proposes Weibo content recommendation algorithm based on text classification. Based on the text mining of a large amount of text information in Weibo platform, the algorithm arranges the user's preference by text classification, and recommends the content for the user by classifying, so as to improve the accuracy and recall rate of recommendation. Finally, the experimental results show that the accuracy and recall rate of the three recommended algorithms have been improved to a certain extent.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.092;TP391.3

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