本文選題：協(xié)作過濾 + 用戶特征��； 參考：《海南大學(xué)》2017年碩士論文

【摘要】：隨著Web2.0技術(shù)的迅速普及,Internet上的數(shù)據(jù)和資源都處于指數(shù)增長(zhǎng)階段,這就會(huì)使用戶面臨信息過載的問題。推薦系統(tǒng)是解決這個(gè)問題的有效辦法之一,其中協(xié)作過濾利用用戶評(píng)分矩陣,計(jì)算用戶之間相似度,并根據(jù)鄰近用戶喜好向目標(biāo)用戶進(jìn)行推薦。但是協(xié)作過濾存在新用戶冷啟動(dòng)、數(shù)據(jù)稀疏性、可拓展性等問題。本文針對(duì)這些問題,利用混合推薦算法的優(yōu)點(diǎn),對(duì)協(xié)作過濾算法進(jìn)行了相應(yīng)的改進(jìn),主要工作如下:(1)梳理歸納了推薦系統(tǒng)的相關(guān)算法,闡述了國(guó)內(nèi)外學(xué)者對(duì)協(xié)作過濾算法的研究現(xiàn)狀。針對(duì)協(xié)作過濾算法存在的缺點(diǎn)進(jìn)行深入分析,并探討如何利用現(xiàn)有技術(shù)來提升協(xié)作過濾推薦算法的性能。(2)針對(duì)冷啟動(dòng)和推薦精度問題,提出了綜合用戶特征及專家信任的協(xié)作過濾推薦算法。通過引入用戶特征,利用用戶填寫的注冊(cè)信息有效緩解推薦系統(tǒng)中冷啟動(dòng)問題。通過引入專家信任,能夠比較用戶與專家的相似性,從而計(jì)算用戶-專家相似度矩陣,進(jìn)而有效降低了數(shù)據(jù)集的稀疏性,提高預(yù)測(cè)的準(zhǔn)確度。從實(shí)驗(yàn)結(jié)果可以看出,該算法能夠有效緩解冷啟動(dòng)問題,明顯提高了系統(tǒng)的推薦精度。(3)針對(duì)數(shù)據(jù)稀疏性和可拓展性問題,提出了基于奇異值分解與K-means++聚類的協(xié)作過濾推薦算法。通過將用戶聚成多個(gè)簇,然后在與目標(biāo)用戶相似的簇中尋找鄰居集,這樣可以緩解數(shù)據(jù)的稀疏性,同時(shí)也降低了計(jì)算量。通過奇異值分解將用戶-項(xiàng)目評(píng)分矩陣進(jìn)行降維,并對(duì)稀疏矩陣進(jìn)行填充,這些模型可在離線的狀態(tài)下進(jìn)行建立。從實(shí)驗(yàn)結(jié)果可以看出,該算法能夠有效緩解稀疏性,并提高推薦精度。
[Abstract]:With the rapid popularization of Web2.0 technology, data and resources on Internet are in an exponential growth stage, which will enable users to face the problem of information overload. Recommendation systems are one of the most effective ways to solve this problem, in which collaborative filtering uses the user rating matrix to calculate the similarity between users and to the target based on the preferences of adjacent users. Users are recommended. But collaborative filtering has problems such as new users' cold start, data sparsity and scalability. In this paper, we use the advantages of the hybrid recommendation algorithm to improve the collaborative filtering algorithm. The main work is as follows: (1) the relevant algorithms of the recommendation system are summarized and summarized, and the scholars at home and abroad are expounded. The current research status of collaborative filtering algorithm is discussed. The shortcomings of collaborative filtering algorithm are deeply analyzed, and how to use existing technology to improve the performance of collaborative filtering recommendation algorithm is discussed. (2) aiming at the problem of cold start and recommendation accuracy, a collaborative filtering recommendation algorithm with comprehensive user characteristics and expert trust is proposed. Using the registration information filled by the user effectively alleviates the cold start problem in the recommendation system. By introducing the expert trust, the similarity between the user and the expert can be compared, and the user expert similarity matrix is calculated, and the sparsity of the data set can be reduced effectively and the accuracy of the prediction can be improved. From the experimental results, it can be seen that the algorithm can be used. Effectively alleviating the cold start problem, it obviously improves the recommendation accuracy of the system. (3) in view of the problem of data sparsity and scalability, a collaborative filtering recommendation algorithm based on singular value decomposition and K-means++ clustering is proposed. By clustering the users into multiple clusters and finding the neighbors in the clusters similar to the target users, the data can be alleviated. By the singular value decomposition, the user project score matrix is reduced and the sparse matrix is filled. These models can be set up in the off-line state. It can be seen from the experimental results that the algorithm can effectively alleviate the sparsity and improve the precision of the recommendation.
【學(xué)位授予單位】：海南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

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