基于加權(quán)動(dòng)態(tài)興趣度的微博推薦方法研究
發(fā)布時(shí)間：2019-01-28 18:30
【摘要】：微博是一種用戶通過關(guān)注關(guān)系進(jìn)行信息實(shí)時(shí)分享的社交網(wǎng)絡(luò)平臺,不同的用戶可能會(huì)有相同的喜好,于是就會(huì)形成具有相同興趣愛好的用戶集體。這就給人們精準(zhǔn)定位用戶興趣取向,為組織機(jī)構(gòu)精準(zhǔn)發(fā)布推薦信息提供了可能性,增加了用戶獲得自己感興趣信息的概率。因此,用戶的興趣度成為微博出現(xiàn)以來人們研究的熱點(diǎn),研究產(chǎn)生了許多個(gè)性化的推薦方法。從現(xiàn)有的研究來看,對微博數(shù)據(jù)進(jìn)行挖掘分析的研究有很多,其中有對微博結(jié)構(gòu)的研究,也有對微博文本的研究。在這些研究模型中,對于微博用戶興趣的模型研究很少,并且沒有考慮到用戶的興趣變化,由于用戶的興趣具有時(shí)間變化性,也就是用戶的興趣會(huì)因?yàn)闀r(shí)間的推移而產(chǎn)生相應(yīng)的變化,可能會(huì)產(chǎn)生興趣轉(zhuǎn)移。基于這一特點(diǎn),本文把時(shí)間作為一個(gè)影響因子引入其中,首先根據(jù)現(xiàn)有的潛在狄利克雷分布模型計(jì)算出微博集數(shù)據(jù)集合的主題分布,從而將用戶個(gè)體的動(dòng)態(tài)興趣度計(jì)算出來;其次,由于用戶之間可能形成具有相同興趣愛好的群體,即可以通過用戶之間的互動(dòng)頻率和相似度,計(jì)算出用戶集合體之間的興趣度,即用戶興趣的相對穩(wěn)定性;再次,將用戶個(gè)體的興趣和用戶興趣集合體的興趣進(jìn)行加權(quán),就可以獲得更加準(zhǔn)確的微博用戶對于微博主題的興趣度;最后,給出一條新的微博,根據(jù)其主題分布,以及新的微博用戶對主題的興趣度,即可計(jì)算出加權(quán)動(dòng)態(tài)興趣度。進(jìn)而,逐一計(jì)算出用戶的加權(quán)動(dòng)態(tài)興趣度,利用興趣度遞減的算法,對所得興趣度進(jìn)行排序,最終將TOP-N個(gè)微博推薦給用戶,從而實(shí)現(xiàn)精準(zhǔn)推薦。論文從模型推薦的總體精度、推薦的時(shí)間精度和不同權(quán)值對模型的影響這幾個(gè)方面對提出的推薦模型進(jìn)行分析,同時(shí)通過實(shí)驗(yàn),將本文提出的算法與基于LDA模型的協(xié)同過濾算法和基于RT-LDA模型的協(xié)同過濾算法進(jìn)行了比較。實(shí)驗(yàn)結(jié)果表明,本文提出的推薦模型比傳統(tǒng)模型可以更為準(zhǔn)確地反映用戶興趣。
[Abstract]:Weibo is a kind of social network platform where users share information in real time by paying attention to the relationship. Different users may have the same preferences, so they will form a group of users with the same interests. This gives people accurate orientation of user interest, provides a possibility for organizations to accurately publish recommendation information, and increases the probability of users getting information of their own interest. Therefore, the interest of users has become the focus of research since Weibo appeared, which has produced many personalized recommendation methods. According to the existing research, there are many researches on Weibo data mining and analysis, including the research on the structure of Weibo and the text of Weibo. Among these research models, there is little research on Weibo user interest model, and it does not take into account the change of user interest, because user interest is time-varying. That is, the interest of the user will change with the passage of time, and may generate a shift of interest. Based on this characteristic, this paper introduces time as an influence factor. Firstly, the topic distribution of Weibo set data set is calculated according to the existing potential Delikley distribution model, and the dynamic interest degree of user is calculated. Secondly, because users may form groups with the same interests, that is, through the interaction frequency and similarity between users, the interest degree between user sets can be calculated, that is, the relative stability of user interest; Thirdly, by weighting the interests of individual users and the interests of users' interest aggregates, we can obtain a more accurate degree of interest of Weibo users to the theme of Weibo; Finally, a new Weibo is given, which can calculate the weighted dynamic interest according to its theme distribution and the interest of the new Weibo user to the topic. Then, the weighted dynamic interest of the user is calculated one by one, and the interest degree is sorted by the algorithm of decreasing interest. Finally, the TOP-N Weibo is recommended to the user, so that the accurate recommendation can be realized. This paper analyzes the recommended model from the following aspects: the overall accuracy of the model, the time accuracy and the influence of different weights on the model. At the same time, through experiments, The proposed algorithm is compared with the collaborative filtering algorithm based on LDA model and the collaborative filtering algorithm based on RT-LDA model. The experimental results show that the proposed recommendation model can reflect user interest more accurately than the traditional model.
【學(xué)位授予單位】：山東科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP393.092;G206
[Abstract]:Weibo is a kind of social network platform where users share information in real time by paying attention to the relationship. Different users may have the same preferences, so they will form a group of users with the same interests. This gives people accurate orientation of user interest, provides a possibility for organizations to accurately publish recommendation information, and increases the probability of users getting information of their own interest. Therefore, the interest of users has become the focus of research since Weibo appeared, which has produced many personalized recommendation methods. According to the existing research, there are many researches on Weibo data mining and analysis, including the research on the structure of Weibo and the text of Weibo. Among these research models, there is little research on Weibo user interest model, and it does not take into account the change of user interest, because user interest is time-varying. That is, the interest of the user will change with the passage of time, and may generate a shift of interest. Based on this characteristic, this paper introduces time as an influence factor. Firstly, the topic distribution of Weibo set data set is calculated according to the existing potential Delikley distribution model, and the dynamic interest degree of user is calculated. Secondly, because users may form groups with the same interests, that is, through the interaction frequency and similarity between users, the interest degree between user sets can be calculated, that is, the relative stability of user interest; Thirdly, by weighting the interests of individual users and the interests of users' interest aggregates, we can obtain a more accurate degree of interest of Weibo users to the theme of Weibo; Finally, a new Weibo is given, which can calculate the weighted dynamic interest according to its theme distribution and the interest of the new Weibo user to the topic. Then, the weighted dynamic interest of the user is calculated one by one, and the interest degree is sorted by the algorithm of decreasing interest. Finally, the TOP-N Weibo is recommended to the user, so that the accurate recommendation can be realized. This paper analyzes the recommended model from the following aspects: the overall accuracy of the model, the time accuracy and the influence of different weights on the model. At the same time, through experiments, The proposed algorithm is compared with the collaborative filtering algorithm based on LDA model and the collaborative filtering algorithm based on RT-LDA model. The experimental results show that the proposed recommendation model can reflect user interest more accurately than the traditional model.
【學(xué)位授予單位】：山東科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP393.092;G206
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 仲兆滿;胡云;李存華;劉宗田;;微博中特定用戶的相似用戶發(fā)現(xiàn)方法[J];計(jì)算機(jī)學(xué)報(bào);2016年04期
2 王莉;程學(xué)旗;;在線社會(huì)網(wǎng)絡(luò)的動(dòng)態(tài)社區(qū)發(fā)現(xiàn)及演化[J];計(jì)算機(jī)學(xué)報(bào);2015年02期
3 高明;金澈清;錢衛(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
4 衛(wèi)冰潔;王斌;;面向微博搜索的時(shí)間感知的混合語言模型[J];計(jì)算機(jī)學(xué)報(bào);2014年01期
5 王振振;何明;杜永萍;;基于LDA主題模型的文本相似度計(jì)算[J];計(jì)算機(jī)科學(xué);2013年12期
6 陳文濤;張小明;李舟軍;;構(gòu)建微博用戶興趣模型的主題模型的分析[J];計(jì)算機(jī)科學(xué);2013年04期
7 田占偉;隋s，
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