本文關(guān)鍵詞： 推薦系統(tǒng) 多屬性 生命周期 DBN 覆蓋率　出處：《新疆大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著數(shù)字信息化時(shí)代的到來(lái),類似于淘寶、京東、亞馬遜等各大網(wǎng)絡(luò)電商的數(shù)量與日俱增,電子商務(wù)個(gè)性化推薦系統(tǒng)亦成為了研究和應(yīng)用的熱門領(lǐng)域。統(tǒng)計(jì)大量研究結(jié)果顯示,目前現(xiàn)有研究方法都是從用戶角度出發(fā)進(jìn)行預(yù)測(cè)和推薦。與傳統(tǒng)的視屏或電影推薦不同,電子商務(wù)個(gè)性化推薦系統(tǒng)不僅要注重用戶體驗(yàn),同時(shí)也要注重商家盈利狀態(tài),因此對(duì)于新項(xiàng)目的推薦、項(xiàng)目推薦的覆蓋率及多樣性成為商家關(guān)注的焦點(diǎn)。如何在現(xiàn)有方法的基礎(chǔ)上從商家角度出發(fā)研究出高質(zhì)量、高性能的推薦技術(shù)就顯得尤其重要。首先,本文提出了基于用戶多屬性的協(xié)同過濾推薦算法(UMACF),該方法從用戶的評(píng)分、評(píng)論、等級(jí)及區(qū)域多因素計(jì)算預(yù)測(cè)評(píng)分值,將預(yù)測(cè)結(jié)果和基于用戶的協(xié)同過濾推薦算法結(jié)合后進(jìn)行推薦。實(shí)驗(yàn)結(jié)果表明:(1)在用戶的評(píng)分、評(píng)論、等級(jí)及區(qū)域4因素中,評(píng)分和評(píng)論是最影響預(yù)測(cè)評(píng)分值的因素;(2)與傳統(tǒng)的協(xié)同過濾推薦算法相比,UMACF推薦算法的預(yù)測(cè)評(píng)分準(zhǔn)確度提高近10%;與UARCF推薦算法相比,UMACF推薦算法的預(yù)測(cè)評(píng)分準(zhǔn)確度提高近5%。其次,本文提出了基于用戶多屬性和項(xiàng)目生命周期的推薦算法(UAIL),該方法根據(jù)評(píng)分、評(píng)論、等級(jí)、區(qū)域、用戶評(píng)論時(shí)間和項(xiàng)目發(fā)布時(shí)間信息使用銷售量增長(zhǎng)率分析法和商家盈利方式構(gòu)建了基于項(xiàng)目生命周期的推薦模型,將該推薦模型和UMACF推薦算法的預(yù)測(cè)評(píng)分值相結(jié)合后進(jìn)行推薦。實(shí)驗(yàn)結(jié)果表明:與UARCF推薦算法相比,覆蓋率提高近28%,推薦新項(xiàng)目的新穎度提高近40%。最后,本文提出了基于DBN分類的協(xié)同過濾推薦算法研究(DBNCF),該方法使用DBN網(wǎng)絡(luò)進(jìn)行學(xué)習(xí)分類,將分類的結(jié)果和UAIL推薦算法的項(xiàng)目生命周期模型結(jié)合形成基于DBN分類的項(xiàng)目生命周期推薦模型,將該模型和UMACF推薦算法的預(yù)測(cè)評(píng)分值相結(jié)合后進(jìn)行推薦。實(shí)驗(yàn)結(jié)果表明:(1)與UAIL推薦算法相比,DBNCF推薦算法的覆蓋率提高5%,推薦新項(xiàng)目的新穎度提高近10%。(2)在時(shí)間耗能方面,UserCF、UARCF、UMACF和UAIL推薦算法時(shí)間消耗較為相近;與這四種推薦算法相比,DBNCF推薦算法需花費(fèi)大量時(shí)間學(xué)習(xí),因此該算法的時(shí)間消耗呈指數(shù)型增長(zhǎng)。
[Abstract]:With the arrival of the digital information age, similar to Taobao, JingDong, Amazon and other major network e-commerce number is increasing day by day. E-commerce personalized recommendation system has also become a hot area of research and application. At present, the existing research methods are from the perspective of users to predict and recommend. Unlike traditional video or film recommendation, e-commerce personalized recommendation system should not only focus on user experience. At the same time, we should also pay attention to the status of business profitability, so for the new project recommendation. The coverage and diversity of project recommendations have become the focus of attention. How to develop high quality and high performance recommendation technology based on existing methods is particularly important. First of all. In this paper, we propose a collaborative filtering recommendation algorithm based on user multi-attribute, which calculates the prediction score from user rating, comment, rank and region multi-factor. The prediction results are combined with the user-based collaborative filtering recommendation algorithm. The experimental results show that: 1) in the user's rating, comment, rating and area of four factors. Scores and comments were the most important factors affecting the predicted scores. Compared with the traditional collaborative filtering recommendation algorithm, the prediction accuracy of UMACF recommendation algorithm is improved by nearly 10%. Compared with the UARCF recommendation algorithm, the prediction accuracy of UMACF recommendation algorithm is improved by nearly 5. Secondly, this paper proposes a recommendation algorithm based on user multi-attribute and project life cycle. According to the rating, comment, rating, region, user comment time and project release time information, the method constructs a recommendation model based on project life cycle using the sales growth rate analysis method and the business profit method. The proposed recommendation model is combined with the prediction score of the UMACF recommendation algorithm. The experimental results show that compared with the UARCF recommendation algorithm, the coverage rate is increased by nearly 28%. Finally, this paper proposes a collaborative filtering recommendation algorithm based on DBN classification, which uses DBN network for learning classification. Combining the result of classification with the project life cycle model of UAIL recommendation algorithm, the project life cycle recommendation model based on DBN classification is formed. The model is combined with the prediction score of the UMACF recommendation algorithm. The experimental results show that the coverage of the UMACF recommendation algorithm is 5% higher than that of the UAIL recommendation algorithm. The time consumption of user CFS UARCF UMACF and UAIL recommendation algorithm is similar to that of UAIL recommendation algorithm. Compared with these four recommendation algorithms, it takes a lot of time to learn, so the time consumption of the proposed algorithm increases exponentially.
【學(xué)位授予單位】：新疆大學(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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