【摘要】：隨著網(wǎng)絡(luò)的高速發(fā)展與普及,人們的生活與網(wǎng)絡(luò)密切相關(guān),網(wǎng)絡(luò)上的購房租房信息過載成為購置或租賃房屋的用戶的一大難題。隨著房屋網(wǎng)站和中介網(wǎng)站中的房屋出租、二手房出售等信息越來越多,部分信息得不到更新,使得用戶很難高效地找到適合自己的房屋信息。相關(guān)網(wǎng)站面臨著的重要問題就是怎樣實(shí)時(shí)并且準(zhǔn)確地向用戶提供其感興趣的房屋,而解決這一問題的有效方法便是個(gè)性化推薦系統(tǒng)。本文作者參加了 365淘房網(wǎng)房屋購買與租賃推薦系統(tǒng)的部分開發(fā)工作,根據(jù)項(xiàng)目的需要,從二部網(wǎng)絡(luò)分析的角度研究了對(duì)房屋的個(gè)性化推薦方法,并設(shè)計(jì)與開發(fā)了相應(yīng)的房屋推薦系統(tǒng),主要研究工作和成果如下:(1)根據(jù)房屋推薦系統(tǒng)的數(shù)據(jù)的特點(diǎn),本文提出了基于二部網(wǎng)絡(luò)社區(qū)挖掘的推薦算法。用帶權(quán)的二部圖來表達(dá)用戶-項(xiàng)目的評(píng)分矩陣。我們提出了二部網(wǎng)絡(luò)社區(qū)挖掘的標(biāo)號(hào)傳遞的算法,先將用戶劃分社區(qū),再將需要推薦的用戶劃分到最相關(guān)的社區(qū),然后利用用戶間的相似性進(jìn)行推薦。算法綜合考慮了用戶與社區(qū)的關(guān)系,以及用戶之間、項(xiàng)目之間的相似性,找出用戶有潛在興趣的項(xiàng)目。實(shí)驗(yàn)結(jié)果說明該算法的推薦結(jié)果比較其他相類似的推薦算法具有較高的精度。(2)提出了將二部網(wǎng)絡(luò)鏈接預(yù)測(cè)中的Jaccard指標(biāo)與奇異值分解相結(jié)合的混合推薦算法。算法先用Jaccard算法求出其推薦房源的相似度指標(biāo)矩陣,然后我們用奇異值分解算法補(bǔ)齊Jaccard指標(biāo)矩陣的零元素,從而得到一張完整的推薦列表。我們提出了對(duì)Jaccard指標(biāo)與奇異值分解動(dòng)態(tài)增量更新的算法,以適應(yīng)評(píng)分表的動(dòng)態(tài)變化。實(shí)驗(yàn)結(jié)果說明,該算法比較其他相關(guān)的推薦算法具有較高的推薦準(zhǔn)確率和召回率。(3)設(shè)計(jì)與開發(fā)了 365淘房網(wǎng)房屋購買與租賃的推薦系統(tǒng),在該系統(tǒng)中應(yīng)用了本文所提出的基于社區(qū)挖掘的推薦算法和Jaccard指標(biāo)與奇異值分解相結(jié)合的混合推薦算法。對(duì)房屋購買與租賃推薦系統(tǒng)進(jìn)行了需求分析,提出系統(tǒng)框架的總體設(shè)計(jì)方案,介紹了系統(tǒng)的模塊結(jié)構(gòu),以及各個(gè)模塊的實(shí)現(xiàn)。
[Abstract]:With the rapid development and popularization of the network, people's life is closely related to the network. The overloading of housing information over the network has become a big problem for the users of the purchase or rental housing. With the housing site and the housing rental in the intermediary websites, the second-hand housing is more and more information and some information can not be updated, making it difficult for the users. The important problem facing the relevant websites is how to provide the real and accurate housing to the users in real time and accurately, and the effective way to solve this problem is the personalized recommendation system. The author participated in the part of the development workers of the 365 house network housing purchase and rental recommendation system. According to the needs of the project, this paper studies the personalized recommendation method of the house from the angle of two network analysis, and designs and develops the corresponding house recommendation system. The main research work and results are as follows: (1) according to the characteristics of the data of the house recommendation system, this paper proposes a recommendation algorithm based on the two network community mining. The two graph is used to express the rating matrix of user project. We put forward two network community mining labeling algorithms. First, we divide the users into the community, then divide the recommended users into the most relevant communities, and then use the similarity between users to recommend them. The algorithm fully considers the relationship between the users and the community, and the users. Between the similarity between the projects and identifying the potential interests of the user, the experimental results show that the proposed results of the algorithm have a higher accuracy compared with other similar recommendation algorithms. (2) a hybrid recommendation algorithm which combines the Jaccard index with the singular value decomposition in the two network link prediction is proposed. The algorithm first uses the algorithm to calculate the algorithm. The similarity index matrix of the recommended house is obtained by the method. Then we use the singular value decomposition algorithm to complement the zero element of the Jaccard index matrix and get a complete recommendation list. We propose an algorithm to update the dynamic increment of the Jaccard index and the singular value decomposition to adapt to the dynamic change of the score table. Compared with other relevant recommendation algorithms, it has high recommendation accuracy and recall rate. (3) a recommendation system for the purchase and lease of 365 house net houses is designed and developed. In this system, a recommendation algorithm based on community mining and a hybrid recommendation algorithm combined with Jaccard index and singular value decomposition are applied. The demand analysis of the purchase and leasing recommendation system is carried out, and the overall design scheme of the system framework is presented. The module structure of the system and the realization of each module are introduced.
【學(xué)位授予單位】：揚(yáng)州大學(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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