本文選題：家裝方案 + 用戶行為�。� 參考：《上海交通大學》2015年碩士論文

【摘要】：近年來互聯(lián)網(wǎng)技術的高速發(fā)展以及人們消費水平的提高,我國的家裝市場電子商務化將越來越全面,推薦引擎類似于我們實際生活中的產(chǎn)品推薦員的作用,為人們在進行家庭裝修過程中選擇合適的家裝方案提供了極大的便利,同時也為家裝網(wǎng)站帶來更多的價值。推薦引擎的使用明顯加快協(xié)同過濾算法的研究和應用,然而在大多數(shù)有關協(xié)同過濾技術的研究只注重理論的算法層面,忽略了在實際應用中的局限性。因此有必要在深入了解家裝網(wǎng)站用戶行為和具體實際情況,針對家裝方案設計一種適合的推薦算法,為有裝修需求的用戶提供家裝方案推薦服務。本文不僅結合了多種協(xié)同過濾算法的思想來設計家裝方案推薦算法,而且還對于家裝網(wǎng)站家裝方案推薦引擎的數(shù)據(jù)收集和處理過程進行詳細闡述。論文的主要工作有以下幾個方面:(1)本文通過對家裝網(wǎng)站用戶行為進行了解之后,研究和分析了協(xié)同過濾算法以及Slope One算法的優(yōu)缺點,并對國內(nèi)外現(xiàn)有的Slope One算法改進方式進行比較和分析,得出在大數(shù)據(jù)量和數(shù)據(jù)相對稀疏的情況下,現(xiàn)有的Slope One算法改進方式將難以獲得較高的家裝方案推薦服務質(zhì)量的結論。由此提出一種結合用戶相似性和項目相似性對Slope One算法進行改進的家裝方案推薦算法。算法主要采用協(xié)同過濾中相似性的概念,同時在項目相似性的計算中,結合了家裝用戶行為評分矩陣中項目相似性以及項目本身相似性兩方面因素。(2)通過對實習公司icolor家裝網(wǎng)站一個月的用戶行為日志進行收集和處理之后,將數(shù)據(jù)分為訓練和測試兩個數(shù)據(jù)集,將新算法與Slope One算法進行比較,實驗通過比較算法的MAE值來證明本文設計的家裝方案推薦算法具有更好的推薦精準度。考慮到不同相似性度量的影響,實驗也對新算法采取不同相似性度量進行比較驗證,得出本文采取余弦定理相似度度量更好的改進策略。(3)將本文設計的家裝方案推薦算法應用到icolor家裝網(wǎng)站中,并對家裝網(wǎng)站推薦引擎進行需求分析和架構設計,從用戶行為日志收集和運輸、離線數(shù)據(jù)預處理和家裝方案算法推薦幾部分進行設計和實現(xiàn),為了避免大量數(shù)據(jù)計算對網(wǎng)站造成的影響,在數(shù)據(jù)進行處理和計算的過程中,結合Hadoop、Hive等大數(shù)據(jù)相關技術以及Mahout技術采取離線的方式進行計算,對涉及的相關數(shù)據(jù)進行數(shù)據(jù)庫設計,最后對家裝網(wǎng)站推薦引擎進行功能測試及界面展示,完成本課題最初期望結果。
[Abstract]:In recent years, with the rapid development of Internet technology and the improvement of people's consumption level, the E-commerce of our home decoration market will be more and more comprehensive, and the recommendation engine will be similar to the role of the product recommender in our real life. It provides great convenience for people to choose suitable home improvement plan in the process of family decoration, and also brings more value to home decoration website. The use of recommendation engine obviously speeds up the research and application of collaborative filtering algorithms. However, most of the researches on collaborative filtering only focus on the theoretical level of algorithms, ignoring the limitations in practical applications. Therefore, it is necessary to deeply understand the user behavior and the actual situation of the home improvement website, and design a suitable recommendation algorithm for the home improvement scheme to provide the home improvement scheme recommendation service for the users with decoration needs. This paper not only combines the idea of various collaborative filtering algorithms to design the home improvement scheme recommendation algorithm, but also describes the data collection and processing process of home improvement website recommendation engine in detail. The main work of this paper is as follows: 1) after understanding the user behavior of home improvement website, this paper studies and analyzes the advantages and disadvantages of collaborative filtering algorithm and Slope One algorithm. By comparing and analyzing the existing improved methods of Slope One algorithm at home and abroad, it is concluded that under the condition of large amount of data and relatively sparse data, it is difficult for the existing improved Slope One algorithm to obtain high quality of service (QoS) of home improvement scheme. This paper proposes a home improvement scheme recommendation algorithm which combines user similarity and item similarity to improve the Slope One algorithm. The algorithm mainly adopts the concept of similarity in collaborative filtering, and at the same time, in the calculation of item similarity, Combining the two factors of item similarity and item itself similarity in the home improvement user behavior score matrix, we collected and processed the user behavior log of icolor Home improvement website for one month. The data is divided into two data sets: training and testing. The new algorithm is compared with the Slope One algorithm, and the MAE value of the algorithm is compared to prove that the recommended algorithm of home improvement scheme designed in this paper has better recommendation accuracy. Considering the influence of different similarity measures, the experiment also compares and verifies that the new algorithm adopts different similarity measures. It is concluded that this paper uses the improved strategy of cosine theorem similarity measure to apply the home improvement scheme recommendation algorithm designed in this paper to the icolor home improvement website, and carries on the requirement analysis and the architecture design to the home improvement website recommendation engine. From the user behavior log collection and transportation, off-line data preprocessing and home improvement scheme algorithm recommended several parts of the design and implementation, in order to avoid the impact of a large amount of data calculation on the website, in the process of data processing and calculation, Combined with big data technology and Mahout technology, the related data are designed, and the function test and interface display of recommendation engine of home decoration website are carried out. Complete the initial expected results of this project.
【學位授予單位】：上海交通大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP391.3

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