【摘要】：近年來,計算機網(wǎng)絡(luò)通信等技術(shù)日益精湛,人們已經(jīng)邁進了一個全新的大數(shù)據(jù)時代,大數(shù)據(jù)時代的到來進一步增加了數(shù)據(jù)膨脹的程度,傳統(tǒng)的信息檢索系統(tǒng)和推薦系統(tǒng)已經(jīng)無法滿足大數(shù)據(jù)環(huán)境下的檢索需求。推薦系統(tǒng)的誕生從某些方面解決了搜索引擎存在的搜索結(jié)果不準確等問題。推薦算法是一個推薦系統(tǒng)的核心,協(xié)同過濾推薦算法是推薦系統(tǒng)領(lǐng)域最經(jīng)典的算法之一。但是在這個全新的大數(shù)據(jù)的背景下,協(xié)同過濾推薦算統(tǒng)也存在著一定的問題,它無法很好地解決評分矩陣稀疏和冷啟動問題。本文針對這兩個問題做了一定的改進,將社交關(guān)系的思想加入到協(xié)同過濾的推薦過程中,并對矩陣補全的方式進行了一定的改進,主要工作如下:一方面,將社交關(guān)系思想加入到相似度計算過程中,得到了基于社交關(guān)系的協(xié)同過濾推薦算法。通過社交關(guān)系數(shù)據(jù)能夠得到每個用戶的好友集合,根據(jù)目標用戶朋友們喜歡的項目或內(nèi)容為目標用戶進行推薦,該算法可以較好地解決基于用戶的冷啟動,能夠提高新用戶的推薦滿意度,本文通過實驗對此進行了驗證。另一方面,條件性地篩選矩陣補全的位置,得到基于社交關(guān)系和條件性補全的協(xié)同過濾推薦算法。該算法在融入社交關(guān)系思想的基礎(chǔ)上對矩陣補全方式進一步改進,選擇滿足一定條件的項進行補全,使得補全后的矩陣更準確,減少了數(shù)據(jù)冗余,該方法能夠很好地解決數(shù)據(jù)稀疏問題,提高算法效率并且使推薦準確率得以提高。本文通過具體的實驗對改進的算法進行了定量的分析和對比,實驗結(jié)果證明改進的算法具有更高的推薦準確率和推薦效率,并且具有較好的MAE、MRSE值。
[Abstract]:In recent years, computer network communication and other technologies have become increasingly sophisticated, people have entered a new era of big data, the arrival of the big data era has further increased the degree of data expansion. Traditional information retrieval systems and recommendation systems can no longer meet the retrieval requirements under the big data environment. The birth of recommendation system solves the problem of inaccuracy of search results in some aspects. Recommendation algorithm is the core of a recommendation system, and collaborative filtering recommendation algorithm is one of the most classical algorithms in the field of recommendation system. However, under the background of this new big data, there are some problems in collaborative filtering recommendation system, which can not solve the problem of sparse score matrix and cold start. In this paper, some improvements have been made to these two problems, the idea of social relations has been added to the recommendation process of collaborative filtering, and the method of matrix complement has been improved. The main work is as follows: on the one hand, The idea of social relationship is added to the similarity calculation process, and a collaborative filtering recommendation algorithm based on social relationship is obtained. Through the social relationship data, each user's friend set can be obtained, and the target user can be recommended according to the items or content that the target user's friends like. The algorithm can solve the cold start based on the user better. It can improve the new user's recommendation satisfaction, this article has carried on the verification through the experiment. On the other hand, a collaborative filtering recommendation algorithm based on social relationship and conditional complement is obtained by selecting the position of matrix complement conditionally. The algorithm further improves the method of matrix complement based on the idea of social relations, and selects the items that meet certain conditions to complement the matrix, which makes the matrix more accurate and reduces the data redundancy. This method can solve the problem of data sparsity, improve the efficiency of the algorithm and improve the accuracy of recommendation. In this paper, quantitative analysis and comparison of the improved algorithm are carried out through specific experiments. The experimental results show that the improved algorithm has higher recommendation accuracy and efficiency, and better MAE / MRSE value.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

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