本文關(guān)鍵詞： 臨時(shí)興趣 三維馬爾科夫模型 混合推薦 興趣閾值　出處：《天津財(cái)經(jīng)大學(xué)》2016年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：隨著大數(shù)據(jù)時(shí)代的到來(lái),網(wǎng)絡(luò)數(shù)據(jù)正在以爆炸式的速度出現(xiàn)在人們生活當(dāng)中。豐富的數(shù)據(jù)資源在給人們帶來(lái)便利的同時(shí)也造成了很大的困擾,如何從龐大的數(shù)據(jù)集中找到期望的數(shù)據(jù)成為了亟待解決的熱門(mén)問(wèn)題。因此,數(shù)據(jù)挖掘技術(shù)逐漸被人們重視起來(lái)。推薦算法是數(shù)據(jù)挖掘技術(shù)的一個(gè)非常重要的應(yīng)用領(lǐng)域。一個(gè)高效并精準(zhǔn)的個(gè)性化推薦方法不僅能夠減少用戶(hù)篩選無(wú)用信息的麻煩,同時(shí)也避免了大量的數(shù)據(jù)傳輸造成的網(wǎng)絡(luò)成本消耗。具有很高的商業(yè)價(jià)值。目前,個(gè)性化推薦系統(tǒng)主要在新聞瀏覽,娛樂(lè)信息瀏覽,電子商務(wù)等領(lǐng)域得到了廣泛的應(yīng)用�；诖�,本文以用戶(hù)能夠更高效更精準(zhǔn)的瀏覽新聞信息的目的出發(fā),提出了一種以引入了興趣閾值參數(shù)的三維馬爾科夫模型推薦算法進(jìn)行精確預(yù)測(cè)并以基于加權(quán)用戶(hù)行為數(shù)據(jù)歸一化的推薦算法進(jìn)行輔助預(yù)測(cè)的推薦策略。在算法的研究過(guò)程中,首先分析了新聞信息區(qū)別其他信息所具有的時(shí)效性強(qiáng)、數(shù)量大的特性。在數(shù)據(jù)整合的過(guò)程中,在排除了特殊情況的前提下,將信息的屬性進(jìn)行了模糊替換以便提取出符合該特性的用戶(hù)偏好特征。其次分析了用戶(hù)臨時(shí)興趣現(xiàn)象和用戶(hù)興趣陡峭式變化的現(xiàn)象帶來(lái)的預(yù)測(cè)不準(zhǔn)的問(wèn)題,并提出在用戶(hù)偏好特征中引入興趣閾值參數(shù)的方式將用戶(hù)的興趣變化及時(shí)反饋給推薦系統(tǒng)的解決辦法。再次依據(jù)該特性和問(wèn)題對(duì)要實(shí)現(xiàn)的新聞推薦算法進(jìn)行需求分析。為了滿(mǎn)足對(duì)算法的需求分析,將Apriori算法以及馬爾科夫模型等算法的特性以及適用性進(jìn)行分析,最終確定了本文的推薦模型,并通過(guò)在模型中引入試探性推送重大主流新聞以及用戶(hù)主動(dòng)反饋的機(jī)制調(diào)整興趣閾值的取值,進(jìn)而改善了用戶(hù)產(chǎn)生臨時(shí)興趣現(xiàn)象和用戶(hù)興趣陡峭式變化的現(xiàn)象帶來(lái)的預(yù)測(cè)不準(zhǔn)的問(wèn)題。然后通過(guò)對(duì)不同參與度用戶(hù)分組實(shí)驗(yàn),對(duì)比了未引入興趣閾值的傳統(tǒng)推薦模型與本文提出的推薦模型在推薦結(jié)果有效性上的差別。驗(yàn)證了本文提出的推薦策略的有效性。最終應(yīng)用本文提出的推薦策略實(shí)現(xiàn)了 一款具有個(gè)性化推薦功能的iOS端的手機(jī)應(yīng)用。
[Abstract]:With the arrival of big data era, network data is appearing in people's lives at an explosive speed. Rich data resources not only bring convenience to people, but also cause a lot of trouble. How to find the expected data from the huge data set has become a hot problem to be solved. Data mining technology has been paid more and more attention. Recommendation algorithm is a very important application field of data mining technology. A highly efficient and accurate personalized recommendation method can not only reduce user filtering useless information. Trouble. At the same time, it also avoids the network cost consumption caused by a large number of data transmission. It has high commercial value. At present, personalized recommendation system mainly in news browsing, entertainment information browsing. Electronic commerce and other fields have been widely used. Based on this, the purpose of this paper is to enable users to browse news information more efficiently and accurately. In this paper, we propose a recommendation strategy based on 3D Markov model recommendation algorithm with interest threshold parameters and a recommendation algorithm based on weighted user behavior data normalization. In the process of research. First of all, the paper analyzes the characteristics of news information in distinguishing other information with strong timeliness and large quantity. In the process of data integration, the special circumstances are excluded. The attribute of information is replaced by fuzzy to extract the characteristic of user preference. Secondly, the problem of uncertain prediction caused by the phenomenon of temporary interest and steep change of interest is analyzed. The method of introducing the threshold parameter of interest into the feature of user preference is put forward to feedback the change of user's interest to the recommendation system in time. According to this feature and problem, the news recommendation algorithm to be implemented is required again. Analysis. In order to meet the needs of the algorithm analysis. The characteristics and applicability of Apriori algorithm and Markov model are analyzed, and the recommendation model of this paper is finally determined. And through the introduction of exploratory push major mainstream news and user active feedback mechanism to adjust the threshold of interest in the model. Then it improves the problem of uncertain prediction caused by the phenomenon of user's temporary interest and the steep change of user's interest. Then the user grouping experiment with different participation degree is carried out. The difference between the traditional recommendation model without interest threshold and the recommendation model proposed in this paper is compared in this paper. The validity of the recommendation strategy proposed in this paper is verified. Finally, the recommendation strategy proposed in this paper is applied. Realized. A personalized recommendation of the iOS end of the mobile application.
【學(xué)位授予單位】：天津財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP391.3

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