【摘要】：近年來(lái),隨著互聯(lián)網(wǎng)服務(wù)的迅速崛起致使用戶與信息量的激增,而在這些海量的數(shù)據(jù)中如何精確、快速的檢索到用戶所需數(shù)據(jù),是目前在大數(shù)據(jù)和數(shù)據(jù)挖掘領(lǐng)域一項(xiàng)重要的研究方向。為了解決這一問(wèn)題,推薦系統(tǒng)以其智能尋求用戶興趣資源的特點(diǎn),顛覆了傳統(tǒng)的文本檢索方式,提供了更加高質(zhì)量的用戶體驗(yàn)。雖然推薦系統(tǒng)極大的改變了用戶對(duì)于信息的獲取方式,但傳統(tǒng)的推薦系統(tǒng)在面對(duì)海量高維的稀疏數(shù)據(jù)時(shí)也會(huì)遇到"冷啟動(dòng)"和"維度災(zāi)難"等問(wèn)題,這些都對(duì)推薦系統(tǒng)的應(yīng)用提出了巨大的挑戰(zhàn)。本文對(duì)現(xiàn)階段主流推薦算法進(jìn)行歸納,對(duì)相似近鄰查找、局部敏感性哈希、協(xié)同過(guò)濾等方面進(jìn)行討論,發(fā)現(xiàn)目前推薦算法在面對(duì)稀疏數(shù)據(jù)方面計(jì)算精度有所下降。同時(shí)在面對(duì)海量高維數(shù)據(jù)時(shí),算法的平均用時(shí)偏長(zhǎng)。為了解決這些問(wèn)題,本文提出了用戶行為特征和動(dòng)態(tài)權(quán)重的概念,將E2LSH算法與混合推薦算法相結(jié)合,構(gòu)建了一個(gè)準(zhǔn)確、高效的推薦系統(tǒng)。本文的主要工作如下:1.針對(duì)推薦算法在稀疏數(shù)據(jù)方面所面臨的推薦精度降低問(wèn)題,本文提出了基于用戶行為特征的動(dòng)態(tài)權(quán)重混合推薦算法。通過(guò)對(duì)原始數(shù)據(jù)集中的數(shù)據(jù)進(jìn)行預(yù)處理,計(jì)算出不同用戶對(duì)于不同物品的個(gè)性化行為特征指數(shù),并將其量化成為用戶行為特征向量,將其引入相似度的計(jì)算中。依據(jù)用戶評(píng)分?jǐn)?shù)據(jù)稀疏性大小的個(gè)性化差異計(jì)算出動(dòng)態(tài)權(quán)重,并依此將基于用戶內(nèi)容的推薦算法和協(xié)同過(guò)濾推薦算法進(jìn)行動(dòng)態(tài)混合。實(shí)驗(yàn)結(jié)果表明,該算法相比于傳統(tǒng)混合推薦算法,其MAE平均降低2.26%,尤其是在數(shù)據(jù)集稀疏性比較極端的情況下,推薦效果的提升更加顯著。2.針對(duì)海量高維數(shù)據(jù)對(duì)混合推薦算法在推薦效率方面的影響,本文研究了基于E2LSH改進(jìn)的混合推薦算法。利用E2LSH算法保持?jǐn)?shù)據(jù)相似一致性的前提下,在系統(tǒng)離線時(shí)構(gòu)建用戶-項(xiàng)目的索引,并在用戶需要在線檢索近鄰時(shí)利用離線索將查找的時(shí)間復(fù)雜度從O(N_2)降低至O(1),在不改變數(shù)據(jù)相似性的情況下來(lái)提高過(guò)濾非相似用戶的計(jì)算效率。從實(shí)驗(yàn)結(jié)果可以看出,該算法在繼續(xù)保持了混合推薦算法計(jì)算精準(zhǔn)性的同時(shí),極大的降低了算法的平均計(jì)算時(shí)間,大大提高了整體計(jì)算效率。3.將本文提出的基于E2LSH改進(jìn)的混合推薦算法初步應(yīng)用到國(guó)家電網(wǎng)網(wǎng)改云檢系統(tǒng)中。在創(chuàng)建任務(wù)選擇任務(wù)對(duì)應(yīng)人員時(shí),系統(tǒng)能夠結(jié)合人員的歷史相關(guān)信息,智能的推薦合適的現(xiàn)場(chǎng)作業(yè)人員,省去用戶手動(dòng)人工篩選的流程,極大的提高了用戶對(duì)于系統(tǒng)的使用體驗(yàn)。
[Abstract]:In recent years, with the rapid rise of Internet services, users and the amount of information surge, and in these massive data how to accurately, quickly retrieve the data users need, It is an important research direction in the field of big data and data mining. In order to solve this problem, the recommendation system based on its intelligent search for user interest resources, subverts the traditional text retrieval methods, and provides a higher quality user experience. Although the recommendation system has greatly changed the way users obtain information, the traditional recommendation system will encounter problems such as "cold start" and "dimension disaster" in the face of massive sparse data of high dimension. These all put forward the huge challenge to the application of the recommendation system. In this paper, the current mainstream recommendation algorithms are summarized, and the similar nearest neighbor lookup, local sensitivity hashing and collaborative filtering are discussed. It is found that the accuracy of the recommendation algorithm in the face of sparse data has been reduced. At the same time, in the face of massive high dimensional data, the average time of the algorithm is too long. In order to solve these problems, this paper proposes the concepts of user behavior characteristics and dynamic weights, and combines E2LSH algorithm with hybrid recommendation algorithm to construct an accurate and efficient recommendation system. The main work of this paper is as follows: 1. Aiming at the problem of low recommendation accuracy in sparse data, a dynamic weighted hybrid recommendation algorithm based on user behavior feature is proposed in this paper. By preprocessing the data in the original data set, the personalized behavior feature index of different users for different items is calculated, and quantized into the user behavior feature vector, which is introduced into the calculation of similarity. The dynamic weight is calculated according to the individualized difference of user rating data sparsity, and the dynamic mixing of user content based recommendation algorithm and collaborative filtering recommendation algorithm is carried out. The experimental results show that compared with the traditional hybrid recommendation algorithm, the MAE of the proposed algorithm is 2.26% lower than that of the traditional hybrid recommendation algorithm, especially when the data set sparsity is extreme, the improvement of the recommendation effect is more significant. 2. Aiming at the effect of massive high dimensional data on the efficiency of hybrid recommendation algorithm, this paper studies the improved hybrid recommendation algorithm based on E2LSH. On the premise of keeping the similarity of data using E2LSH algorithm, the index of user-item is constructed when the system is offline. The time complexity of searching is reduced from O (N _ S _ 2) to O (1) when users need to search their nearest neighbors online, so as to improve the computing efficiency of filtering dissimilar users without changing the similarity of data. The experimental results show that the algorithm not only keeps the accuracy of the hybrid recommendation algorithm, but also greatly reduces the average computing time of the algorithm and greatly improves the overall calculation efficiency. The proposed hybrid recommendation algorithm based on E2LSH is applied to the cloud detection system of State Grid. When creating the task selection task counterpart, the system can intelligently recommend the appropriate field operator according to the historical information of the personnel, so as to eliminate the flow of manual screening by the user. It greatly improves the user's experience of using the system.
【學(xué)位授予單位】：武漢大學(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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