發(fā)布時(shí)間：2019-02-16 10:27

【摘要】：互聯(lián)網(wǎng)中信息規(guī)模的爆炸式增長(zhǎng),滿足了用戶對(duì)信息的需求。但龐大的信息量使得用戶難以快速定位到有用信息,降低了信息的使用率,導(dǎo)致了信息過(guò)載問(wèn)題的出現(xiàn)。個(gè)性化推薦技術(shù)是一種面向用戶進(jìn)行個(gè)性化推薦的有效手段,其核心是推薦算法。Slope One算法是一種簡(jiǎn)單而高效的基于項(xiàng)目的協(xié)同過(guò)濾算法,能夠在少量數(shù)據(jù)情況下達(dá)到較好的推薦效果,已經(jīng)得到了廣泛應(yīng)用。但現(xiàn)有的Slope One算法無(wú)法在數(shù)據(jù)稀疏情況下做精確推薦,評(píng)分過(guò)程中會(huì)利用無(wú)關(guān)項(xiàng)目預(yù)測(cè)評(píng)分且無(wú)法快速感知用戶興趣的變化。為了解決上述問(wèn)題,本文對(duì)權(quán)重的計(jì)算方法加以改進(jìn),提出改進(jìn)的加權(quán)Slope One算法,再引入數(shù)據(jù)挖掘的相關(guān)技術(shù),對(duì)數(shù)據(jù)進(jìn)行分類和預(yù)處理,提出基于聚類的加權(quán)Slope One算法。所做的主要工作如下:第一,在傳統(tǒng)的K-Means算法的基礎(chǔ)上,提出一種自動(dòng)生成K個(gè)聚類中心的基于最小生成樹(shù)的K-Means算法,有效解決傳統(tǒng)的K-Means算法因初始聚類中心選取的隨機(jī)性引起的局部最優(yōu)問(wèn)題,提高聚類效果;第二,利用聚類結(jié)果對(duì)原始項(xiàng)目評(píng)分矩陣進(jìn)行預(yù)測(cè)填充,解決算法存在的稀疏性問(wèn)題,并根據(jù)聚類結(jié)果縮小推薦候選集的規(guī)模,減少推薦算法計(jì)算量;第三,考慮項(xiàng)目屬性和項(xiàng)目評(píng)分對(duì)項(xiàng)目相似度影響程度的不同,引入項(xiàng)目屬性和項(xiàng)目評(píng)分的項(xiàng)目綜合相似度計(jì)算方法,提高項(xiàng)目相似度的準(zhǔn)確性;第四,為了在算法中更好的反應(yīng)用戶興趣的變化,突出新數(shù)據(jù)作用削弱舊數(shù)據(jù)。在推薦算法中加入時(shí)間權(quán)重,考慮影響時(shí)間權(quán)重的因素,提出加入訪問(wèn)頻率的時(shí)間權(quán)重函數(shù);第五,根據(jù)本文提出的改進(jìn)算法,設(shè)計(jì)推薦系統(tǒng),介紹系統(tǒng)中模塊組成、模塊間調(diào)用關(guān)系和模塊內(nèi)部算法流程,利用MovieLens數(shù)據(jù)集在系統(tǒng)上進(jìn)行驗(yàn)證。實(shí)驗(yàn)證明,基于聚類的加權(quán)Slope One算法與傳統(tǒng)推薦算法相比,聚類算法的加入能夠有效解決稀疏性問(wèn)題,減少計(jì)算量;項(xiàng)目相似度和時(shí)間權(quán)重的加入提高了算法預(yù)測(cè)的準(zhǔn)確性和時(shí)間敏感度。整體算法在平均絕對(duì)誤差上有著明顯的降低,能夠有效提高推薦系統(tǒng)整體性能。
[Abstract]:The explosive growth of the scale of information in the Internet meets the needs of users for information. However, the huge amount of information makes it difficult for users to locate useful information quickly, reduce the utilization rate of information, and lead to the problem of information overload. Personalized recommendation technology is an effective way for users to make personalized recommendation. Its core is that the recommendation algorithm. Slope One algorithm is a simple and efficient collaborative filtering algorithm based on project. It has been widely used to achieve good recommendation effect in a small amount of data. However, the existing Slope One algorithm can not make accurate recommendation in the case of sparse data. In the process of evaluation, independent items are used to predict the score and the changes of user interest can not be quickly perceived. In order to solve the above problems, this paper improves the method of weight calculation, proposes an improved weighted Slope One algorithm, introduces the related technology of data mining, classifies and preprocesses the data, and proposes a weighted Slope One algorithm based on clustering. The main works are as follows: first, based on the traditional K-Means algorithm, a K-Means algorithm based on minimum spanning tree is proposed to generate K clustering centers automatically. In order to improve the clustering effect, the traditional K-Means algorithm can solve the local optimal problem caused by the randomness of the initial clustering center selection. Secondly, the original item scoring matrix is predicted and filled with the clustering results to solve the sparse problem of the algorithm. According to the clustering results, the size of the recommended candidate set is reduced, and the calculation amount of the recommendation algorithm is reduced. Thirdly, considering the difference between item attribute and item score on project similarity, we introduce the method of project attribute and item score to calculate the project similarity, and improve the accuracy of project similarity. Fourth, in order to better reflect the change of user interest in the algorithm, highlight the role of new data weakening the old data. The time weight is added to the recommendation algorithm, and the time weight function of the access frequency is put forward considering the factors that affect the time weight. Fifthly, according to the improved algorithm proposed in this paper, we design the recommendation system, introduce the module composition, the call relationship between modules and the algorithm flow inside the module, and use the MovieLens data set to verify the system. The experiments show that compared with the traditional recommendation algorithm, the weighted Slope One algorithm based on clustering can effectively solve the sparse problem and reduce the computational complexity. The addition of item similarity and time weight improves the accuracy and time sensitivity of the algorithm. The overall algorithm can significantly reduce the average absolute error and can effectively improve the overall performance of the recommendation system.
【學(xué)位授予單位】：北京工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.3

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