發(fā)布時(shí)間：2018-03-03 09:56

  本文選題：LM-BP神經(jīng)網(wǎng)絡(luò)　切入點(diǎn)：信息熵　出處：《北京交通大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來,隨著互聯(lián)網(wǎng)技術(shù)的發(fā)展,網(wǎng)絡(luò)數(shù)據(jù)越來越龐大,用戶怎樣從眾多的數(shù)據(jù)中更快速的捕捉到自己感興趣的數(shù)據(jù)信息成為網(wǎng)絡(luò)技術(shù)發(fā)展的研究熱點(diǎn)。學(xué)術(shù)界和業(yè)界對這種信息過載問題開展了大量的研究和實(shí)踐工作,提出了多種形式的信息個(gè)性化解決方案。推薦系統(tǒng)作為一種智能個(gè)性化信息服務(wù)系統(tǒng),具有用戶需求驅(qū)動(dòng)、主動(dòng)服務(wù)和信息個(gè)性化程度高等優(yōu)點(diǎn),在電子商務(wù)、在線學(xué)習(xí)和數(shù)字圖書館等領(lǐng)域得到了廣泛應(yīng)用,并已成為公認(rèn)最有前途的信息個(gè)性化技術(shù)發(fā)展方向。協(xié)同過濾推薦算法是推薦系統(tǒng)中最成功的技術(shù)之一,雖然已經(jīng)被應(yīng)用超過了十年,但是由于商品量越來越多,用戶評價(jià)和購買的數(shù)量有限,數(shù)據(jù)稀疏性問題越來越嚴(yán)重,另外還存在冷啟動(dòng)等問題,導(dǎo)致協(xié)同過濾推薦算法的準(zhǔn)確性還有待提高。針對數(shù)據(jù)稀疏性問題,本文設(shè)計(jì)了新的協(xié)同過濾算法,根據(jù)用戶評分交集的大小選擇用戶最近鄰居集,采用LM-BP神經(jīng)網(wǎng)絡(luò)對用戶一項(xiàng)目的評分矩陣進(jìn)行估值填充,提高評分矩陣的密度。這種方法避免了傳統(tǒng)降維法導(dǎo)致信息缺失的缺點(diǎn),能提高預(yù)測值的準(zhǔn)確度,從而提高協(xié)同過濾推薦系統(tǒng)的推薦質(zhì)量。相似度的計(jì)算是協(xié)同過濾的一個(gè)重要步驟,傳統(tǒng)的計(jì)算方法容易夸大或縮小相似性,從而影響推薦質(zhì)量。本文采用信息熵的方法來計(jì)算相似度,通過計(jì)算評分差值的信息熵,并將用戶評分差異和交疊程度加權(quán)到公式中,提高相似度計(jì)算的準(zhǔn)確度,從而提高協(xié)同過濾推薦算法的推薦效果。最后,本文采用了Movielens的真實(shí)數(shù)據(jù)集進(jìn)行實(shí)踐研究,用Matlab對本文提出的基于LM-BP神經(jīng)網(wǎng)絡(luò)的推薦算法進(jìn)行測試。從準(zhǔn)確度、平均絕對誤差、召回率、F1指標(biāo)四個(gè)方面,相似度求法、和估值填充兩個(gè)角度,對本文提出的推薦算法的效果進(jìn)行了研究。數(shù)據(jù)結(jié)果表明基于LM-BP神經(jīng)網(wǎng)絡(luò)的協(xié)同過濾推薦算法比傳統(tǒng)的協(xié)同過濾推薦算法有更好的推薦效果。
[Abstract]:In recent years, with the development of Internet technology, the network data become more and more huge. How users can quickly capture the information of their interest from a large number of data has become a research hotspot in the development of network technology. Academic circles and industry have carried out a lot of research and practice on this kind of information overload. As an intelligent personalized information service system, recommendation system has the advantages of user demand driven, active service and high degree of information personalization. Online learning and digital library have been widely used, and have become the most promising development direction of personalized information technology. Collaborative filtering recommendation algorithm is one of the most successful technologies in recommendation system. Although it has been used for more than a decade, due to the increasing volume of commodities, the limited number of user evaluations and purchases, the problem of data sparsity is becoming more and more serious, and there are also problems such as cold startup. The accuracy of collaborative filtering recommendation algorithm still needs to be improved. Aiming at the problem of data sparsity, this paper designs a new collaborative filtering algorithm, which selects the nearest neighbor set according to the size of the user score intersection. The LM-BP neural network is used to populate the evaluation matrix of a user item to increase the density of the scoring matrix. This method avoids the shortcoming of the traditional dimensionality reduction method which leads to the lack of information, and can improve the accuracy of the prediction value. In order to improve the recommendation quality of collaborative filtering recommendation system, the calculation of similarity is an important step in collaborative filtering, and the traditional calculation method is easy to exaggerate or reduce the similarity. In this paper, the information entropy method is used to calculate the similarity, and the information entropy of the score difference is calculated, and the user score difference and overlapping degree are weighted to the formula to improve the accuracy of the similarity calculation. In order to improve the recommendation effect of collaborative filtering recommendation algorithm. Finally, this paper uses the real data set of Movielens for practical research, and uses Matlab to test the recommendation algorithm based on LM-BP neural network. Recall rate and F1 index four aspects, similarity method, and valuation filling two angles, The results show that the collaborative filtering recommendation algorithm based on LM-BP neural network has better recommendation effect than the traditional collaborative filtering recommendation algorithm.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.3;TP183

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