本文選題：推薦系統(tǒng) + 協(xié)同過濾��； 參考：《蘭州財(cái)經(jīng)大學(xué)》2017年碩士論文

【摘要】：大數(shù)據(jù)時(shí)代,互聯(lián)網(wǎng)信息和資源嚴(yán)重過載,電子商務(wù)網(wǎng)站的商品數(shù)量和用戶數(shù)量每天都在增長,人們在商品選擇的過程中花費(fèi)的時(shí)間和精力也越來越多。搜索引擎和推薦技術(shù)應(yīng)運(yùn)而生,推薦系統(tǒng)以其能夠提供個(gè)性化服務(wù)和能夠精確匹配用戶需求成為當(dāng)下研究的熱點(diǎn)。從20世紀(jì)90年代推薦系統(tǒng)誕生以來,協(xié)同過濾推薦算法就受到了眾多研究學(xué)者的青睞,而且也已經(jīng)在實(shí)際應(yīng)用中發(fā)揮作用,比如亞馬遜網(wǎng)站就是利用協(xié)同過濾技術(shù)進(jìn)行推薦的,而且有35%的銷售額和推薦技術(shù)有關(guān)。隨著網(wǎng)絡(luò)信息越來越復(fù)雜,協(xié)同過濾技術(shù)的不足也顯露了出來。本文從用戶興趣的角度分析用戶行為,將用戶的行為信息融入到協(xié)同過濾算法的推薦過程中,首先對用戶興趣進(jìn)行分類和描述,將用戶興趣分為用戶客觀興趣、用戶主觀興趣和用戶綜合興趣;然后分別基于用戶客觀興趣模型和用戶主觀興趣模型改進(jìn)傳統(tǒng)的協(xié)同過濾的算法:1)提出了基于用戶客觀興趣改進(jìn)的協(xié)同過濾推薦算法——OICF算法,采用模糊C均值聚類技術(shù)得到用戶客觀興趣度,填充評分矩陣來改進(jìn)基于項(xiàng)目的協(xié)同過濾算法,緩解了由于數(shù)據(jù)稀疏性帶來的推薦不準(zhǔn)確的問題;2)提出了基于用戶主觀興趣改進(jìn)的協(xié)同過濾推薦算法——SICF算法,通過統(tǒng)計(jì)的方法,構(gòu)建基于評級的主觀興趣度矩陣,代替評分矩陣計(jì)算相似度,改進(jìn)基于用戶的協(xié)同過濾算法,改善由于用戶數(shù)量和項(xiàng)目數(shù)量的增長,評分矩陣越來越大給計(jì)算性能造成影響的問題;3)采用兩階段的加權(quán)綜合方法對改進(jìn)后的算法進(jìn)行組合,得到了基于用戶綜合興趣改進(jìn)的協(xié)同過濾推薦算法——UICF算法,推薦準(zhǔn)確度得到一定程度的提升,并且每種改進(jìn)效果均在movielens 100k數(shù)據(jù)集上進(jìn)行驗(yàn)證;最后根據(jù)CRISP-DM過程模型的基本流程用Book-crossing數(shù)據(jù)集對改進(jìn)算法進(jìn)行了進(jìn)一步的驗(yàn)證。
[Abstract]:In the big data age, the Internet information and resources are overloaded, the number of goods and the number of users of e-commerce websites are increasing every day. People spend more and more time and energy in the process of commodity selection. The search engine and recommendation technology emerge as the times require, and the recommendation system can provide personalized services and accurate matching. User demand has become a hot spot in the present research. Since the birth of the recommendation system in 1990s, collaborative filtering recommendation algorithm has been favored by many researchers, and it has also played a role in practical applications, such as Amazon Web site is recommended by collaborative filtering technology, and there are 35% sales and recommendation technology. As the network information becomes more and more complex, the shortage of collaborative filtering technology is also revealed. This paper analyzes user behavior from the angle of user interest, and integrates user's behavior information into the recommendation process of collaborative filtering algorithm. First, the user interest is classified and described, and user interest is divided into user's objective interest, and user's interest is divided into user interest. Subjective interest and user's comprehensive interest, and then based on the user objective interest model and the user's subjective interest model to improve the traditional collaborative filtering algorithm: 1) a collaborative filtering recommendation algorithm based on the user's objective interest improvement is proposed, OICF algorithm, and the fuzzy C mean clustering technique is used to obtain the user's objective interest degree and fill the scoring moment. A collaborative filtering algorithm based on project is improved to alleviate the problem of inaccurate recommendation caused by data sparsity. 2) a collaborative filtering recommendation algorithm based on user's subjective interest improvement, SICF algorithm, is proposed. By statistical method, the subjective interest matrix based on rating is constructed and the similarity degree is calculated instead of the score matrix. Into the user based collaborative filtering algorithm to improve the increasing number of users and the number of projects, the scoring matrix is becoming more and more influential to the computing performance. 3) the improved algorithm is combined with the two stage weighted synthesis method, and a collaborative filtering recommendation algorithm based on the improvement of the user's comprehensive interest - UICF calculation is obtained. Method, the recommendation accuracy is improved to a certain extent, and each improvement effect is verified on the movielens 100k data set. Finally, the improved algorithm is further verified with the Book-crossing data set based on the basic process of the CRISP-DM process model.

【學(xué)位授予單位】：蘭州財(cái)經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

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