本文選題：互惠推薦 + 在線交友��； 參考：《山東師范大學(xué)》2017年碩士論文

【摘要】：隨著電子商務(wù)的發(fā)展,出現(xiàn)了越來越多的用戶到用戶模式的雙向推薦,傳統(tǒng)個(gè)性化推薦的項(xiàng)目到用戶的推薦已經(jīng)無法滿足用戶的需求,這就催生了時(shí)下以在線交友網(wǎng)站為代表的互惠推薦的蓬勃發(fā)展。在線交友網(wǎng)站每天為進(jìn)行交友的異性雙方提供大量的交友信息,求職網(wǎng)站每天不僅為求職者提供大量的職位信息,而且為企業(yè)的人員招聘提供大量的求職簡(jiǎn)歷信息,但是這些信息繁雜龐大,缺乏有效的系統(tǒng)分類,無論是對(duì)交友雙方還是對(duì)參與到求職招聘環(huán)節(jié)中的求職人員和招聘人員,都難以精確定位自己感興趣的對(duì)象。因此,改進(jìn)互惠推薦算法并且最大程度上提高推薦質(zhì)量是迫切需要解決的。本文提出面向在線交友領(lǐng)域的互惠推薦算法,從矩陣補(bǔ)全、互惠相似度兩個(gè)方面來提高互惠推薦算法的效率,充分考慮兩個(gè)方面之間的關(guān)系,對(duì)互惠推薦算法進(jìn)行了較為深入的研究工作。主要工作及貢獻(xiàn)如下:(1)針對(duì)以在線交友為代表的互惠推薦系統(tǒng)中存在的數(shù)據(jù)稀疏問題,提出一種緩解數(shù)據(jù)稀疏性的矩陣補(bǔ)全算法。首先,深入分析了常用的兩種矩陣補(bǔ)全方法;其次,在此基礎(chǔ)上將兩種方法進(jìn)行混合加權(quán),提出了基于LMaFit和K-Means混合加權(quán)的矩陣補(bǔ)全算法,將低秩矩陣補(bǔ)全LMaFit算法和K-Means聚類算法的優(yōu)勢(shì)進(jìn)行互補(bǔ);最后,通過實(shí)驗(yàn)驗(yàn)證了基于LMaFit和K-Means混合加權(quán)的矩陣補(bǔ)全算法比任何單獨(dú)一種在平均絕對(duì)誤差MAE上都表現(xiàn)得要好。(2)提出一種基于互惠相似度的互惠推薦算法。首先,對(duì)男女用戶的顯式偏好和隱式偏好做了相關(guān)定義,并在此基礎(chǔ)上給出了男女用戶之間顯式偏好相似度和隱式偏好相似度的計(jì)算方法;其次,針對(duì)顯式偏好相似度和隱式偏好相似度在男女用戶互惠推薦中作用的大小,分別賦予不同的權(quán)重因子,形成改進(jìn)后的互惠相似度;最后,與當(dāng)前互惠推薦中常用的兩種推薦算法進(jìn)行了實(shí)驗(yàn)對(duì)比,該算法在準(zhǔn)確率、召回率和調(diào)和平均數(shù)方面,比另外兩種算法都有明顯地改善。
[Abstract]:With the development of e-commerce, more and more users to the user mode of two-way recommendation, the traditional personalized recommendation from the item to the user recommendation has been unable to meet the needs of users. This gave birth to the rapid development of reciprocal recommendations, as represented by online dating sites. Online dating sites provide a lot of dating information for the opposite sex who make friends every day. Job search websites not only provide a lot of job information for job seekers every day, but also provide a lot of resume information for the recruitment of people in enterprises. However, these information are complicated and lack of effective systematic classification. It is difficult to accurately locate the object of interest to both friends and job seekers and recruiters who are involved in the job recruitment process. Therefore, it is urgent to improve the reciprocal recommendation algorithm and improve the recommendation quality to the greatest extent. In this paper, a reciprocal recommendation algorithm for online dating is proposed, which improves the efficiency of the reciprocal recommendation algorithm from two aspects of matrix complement and reciprocal similarity, and fully considers the relationship between the two aspects. The reciprocal recommendation algorithm is studied deeply. The main work and contributions are as follows: (1) aiming at the problem of data sparsity in the reciprocal recommendation system represented by online dating, a matrix complement algorithm is proposed to reduce the data sparsity. Firstly, two kinds of matrix complement methods are deeply analyzed, secondly, the two methods are mixed weighted, and a matrix complement algorithm based on LMaFit and K-Means is proposed. The advantages of the low rank matrix complement LMaFit algorithm and the K-Means clustering algorithm are complementary. Finally, Experimental results show that the matrix complement algorithm based on LMaFit and K-Means is better than any single one on the average absolute error (MAE).) A reciprocal recommendation algorithm based on reciprocal similarity is proposed. Firstly, the explicit preference and implicit preference of male and female users are defined, and on this basis, the explicit preference similarity and implicit preference similarity between male and female users are calculated. According to the magnitude of explicit preference similarity and implicit preference similarity in the reciprocal recommendation of male and female users, different weight factors are assigned to form the improved reciprocal similarity. Finally, The experimental results are compared with the two commonly used recommendation algorithms in the reciprocal recommendation system. The proposed algorithm is much better than the other two algorithms in terms of accuracy recall and harmonic average.
【學(xué)位授予單位】：山東師范大學(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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