發(fā)布時(shí)間：2018-06-02 04:21

  本文選題：用戶興趣 + 用戶標(biāo)簽　； 參考：《山東師范大學(xué)》2017年碩士論文

【摘要】：社交網(wǎng)絡(luò)中的微博平臺(tái),近年來(lái)得到了廣大用戶的喜愛(ài)和關(guān)注。據(jù)了解,每天都會(huì)有不計(jì)其數(shù)的新用戶加入該平臺(tái),并在平臺(tái)上留下成千上萬(wàn)條信息。面對(duì)海量的微博信息,用戶總是在不停地尋找與自己興趣相一致的信息,那么如何從這些信息中發(fā)現(xiàn)用戶的興趣,并向其推薦感興趣的微博,成為目前研究的一個(gè)熱點(diǎn)問(wèn)題。本文正是以此為出發(fā)點(diǎn),針對(duì)微博推薦算法中所存在的問(wèn)題進(jìn)行相關(guān)的研究。首先,針對(duì)在用戶興趣挖掘階段中存在的準(zhǔn)確率不高的問(wèn)題,本文提出了一種基于標(biāo)簽更新的微博用戶興趣挖掘算法;其次,針對(duì)微博推薦階段中存在的冷啟動(dòng)問(wèn)題,本文提出了一種融合標(biāo)簽與人工蜂群的微博推薦算法;最后,利用上述兩種算法設(shè)計(jì)與實(shí)現(xiàn)了微博推薦原型系統(tǒng)。本文所研究的具體工作如下:(1)研究用戶興趣挖掘算法,提出一種基于標(biāo)簽更新的微博用戶興趣挖掘算法。首先,根據(jù)標(biāo)簽的多種特征,用戶建立自身的初始興趣;其次,利用用戶關(guān)注人與用戶間的相似度、用戶關(guān)注人自身的影響力和用戶關(guān)注人與用戶間的親密度三種關(guān)系計(jì)算標(biāo)簽的更新強(qiáng)度;最后,根據(jù)標(biāo)簽的更新規(guī)則對(duì)標(biāo)簽進(jìn)行更新建立用戶興趣模型。該方法在準(zhǔn)確率和召回率方面都有一定的提高,說(shuō)明運(yùn)用該方法表示用戶興趣具有一定的有效性。(2)研究微博推薦算法,提出一種融合標(biāo)簽和人工蜂群的微博推薦算法。首先,對(duì)用戶標(biāo)簽信息進(jìn)行定義;其次,利用已定義的標(biāo)簽權(quán)重、標(biāo)簽偏好和標(biāo)簽與微博中詞語(yǔ)的相似度三種變量來(lái)構(gòu)造人工蜂群中的適應(yīng)度函數(shù);最后,利用人工蜂群算法的搜索策略,搜索出具有最優(yōu)適應(yīng)度值的微博向用戶進(jìn)行推薦。該方法不僅可以解決推薦算法中的冷啟動(dòng)問(wèn)題,而且對(duì)提高推薦算法的準(zhǔn)確性也具有良好的效果。(3)設(shè)計(jì)與實(shí)現(xiàn)基于用戶興趣的微博推薦原型系統(tǒng)的。以上述兩種算法為理論基礎(chǔ),分析和詳細(xì)設(shè)計(jì)系統(tǒng)中所需的各個(gè)模塊和流程,并最終實(shí)現(xiàn)基于用戶興趣的微博推薦原型系統(tǒng),以供用戶及時(shí)發(fā)現(xiàn)并找到自己所喜愛(ài)的微博信息。
[Abstract]:In recent years, the Weibo platform in the social network has been loved and concerned by the majority of users. Countless new users join the platform every day and leave thousands of messages on it. In the face of massive Weibo information, users are always looking for information consistent with their own interests, so how to find the interest of users from these information and recommend interested Weibo to them has become a hot issue. In this paper, we study the problems in Weibo recommendation algorithm. Firstly, aiming at the problem that the accuracy of user interest mining is not high, this paper proposes a new Weibo user interest mining algorithm based on tag updating. Secondly, aiming at the cold start problem in Weibo recommendation phase, In this paper, a Weibo recommendation algorithm combining tag and artificial bee colony is proposed, and finally, the prototype system of Weibo recommendation is designed and implemented by using the above two algorithms. The main work of this paper is as follows: 1) A new Weibo user interest mining algorithm based on tag update is proposed. First of all, according to the various features of the label, the user establishes his own initial interest; secondly, the user focuses on the similarity between the user and the user. The user pays attention to the influence of the person and the user pays close attention to the relationship between the user and the user to calculate the update intensity of the label. Finally, the user interest model is established according to the update rules of the label. This method has some improvement in accuracy and recall rate. It shows that it is effective to use this method to express user's interest. (2) to study the Weibo recommendation algorithm, and to propose a Weibo recommendation algorithm combining tag and artificial bee colony. Firstly, the user tag information is defined; secondly, the fitness function in artificial bee colony is constructed by using the defined tag weight, label preference and label similarity with words in Weibo. Using the search strategy of artificial bee colony algorithm, the Weibo with the optimal fitness value is searched for recommendation to the user. This method can not only solve the cold start problem in the recommendation algorithm, but also improve the accuracy of the recommendation algorithm. It has a good effect in designing and implementing the Weibo recommendation prototype system based on the user's interest. Based on the above two algorithms, this paper analyzes and designs each module and flow of the system in detail, and finally realizes the prototype system of Weibo recommendation based on user's interest, so that users can find and find their favorite Weibo information in time.
【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(lèi)號(hào)】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 徐雅斌;孫曉晨;;位置社交網(wǎng)絡(luò)的個(gè)性化位置推薦[J];北京郵電大學(xué)學(xué)報(bào);2015年05期

2 王桐;曲桂雪;;基于柯西分布量子粒子群的混合推薦算法[J];中南大學(xué)學(xué)報(bào)(自然科學(xué)版);2015年08期

3 叢穎;劉其成;張偉;;一種基于Apriori的微博推薦并行算法[J];計(jì)算機(jī)應(yīng)用與軟件;2015年08期

4 張紹武;尹杰;林鴻飛;魏現(xiàn)輝;;基于用戶分析的微博用戶影響力度量模型[J];中文信息學(xué)報(bào);2015年04期

5 蔡淑琴;袁乾;周鵬;梁烽;;基于信息傳播理論的微博協(xié)同過(guò)濾推薦模型[J];系統(tǒng)工程理論與實(shí)踐;2015年05期

6 彭澤環(huán);孫樂(lè);韓先培;陳波;;社區(qū)熱點(diǎn)微博推薦研究[J];計(jì)算機(jī)研究與發(fā)展;2015年05期

7 于洪濤;崔瑞飛;董芹芹;;基于遺忘曲線的微博用戶興趣模型[J];計(jì)算機(jī)工程與設(shè)計(jì);2014年10期

8 碩良勛;柴變芳;張新東;;基于改進(jìn)最近鄰的協(xié)同過(guò)濾推薦算法[J];計(jì)算機(jī)工程與應(yīng)用;2015年05期

9 扈維;張堯?qū)W;周悅芝;;基于社會(huì)化標(biāo)注的用戶興趣挖掘[J];清華大學(xué)學(xué)報(bào)(自然科學(xué)版);2014年04期

10 高明;金澈清;錢(qián)衛(wèi)寧;王曉玲;周傲英;;面向微博系統(tǒng)的實(shí)時(shí)個(gè)性化推薦[J];計(jì)算機(jī)學(xué)報(bào);2014年04期

相關(guān)碩士學(xué)位論文 前1條

1 孫威;微博用戶興趣挖掘與建模研究[D];大連理工大學(xué);2012年

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