本文選題：協(xié)同過濾 + 推薦算法　； 參考：《北京郵電大學(xué)》2017年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)的不斷發(fā)展和人們消費(fèi)觀念的轉(zhuǎn)變,電子商務(wù)平臺(tái)成為了人們首要的購物方式。在這種情況下,個(gè)性化推薦系統(tǒng)成為了幫助用戶快速發(fā)現(xiàn)喜好商品的主要方式,同時(shí)也成為了幫助平臺(tái)銷售商品的主要手段。在推薦系統(tǒng)中,協(xié)同過濾推薦算法是被最廣泛使用的,該算法的主要思想是尋找目標(biāo)用戶的相似鄰居集,將該鄰居集的用戶喜好作為推薦項(xiàng)推薦給目標(biāo)用戶。但是,隨著移動(dòng)互聯(lián)網(wǎng)的高速發(fā)展,傳統(tǒng)的協(xié)同過濾推薦算法面臨著更多挑戰(zhàn)。首先,在移動(dòng)互聯(lián)網(wǎng)環(huán)境下,電子商務(wù)平臺(tái)可以獲取更多關(guān)于用戶的信息內(nèi)容,諸如用戶位置上下文信息。這些信息對(duì)推薦算法中計(jì)算用戶之間的相似性產(chǎn)生了巨大的影響。其次,移動(dòng)互聯(lián)網(wǎng)為推薦算法帶來了更加龐大的數(shù)據(jù)量,在大數(shù)據(jù)環(huán)境下,推薦算法的推薦實(shí)時(shí)性難以得到有效的保障。針對(duì)上述移動(dòng)互聯(lián)網(wǎng)環(huán)境下推薦算法面臨的兩個(gè)問題,本文進(jìn)行了深入的研究分析,提出了相應(yīng)的解決方案。針對(duì)移動(dòng)互聯(lián)網(wǎng)環(huán)境下用戶位置上下文信息對(duì)推薦算法中用戶間相似性計(jì)算影響的問題,本文提出一種基于位置上下文信息的協(xié)同過濾推薦算法(CFLC, A Collaborative Filtering Algorithm Based on Location Context)。該算法將用戶位置信息作為關(guān)鍵影響因素,與修正的余弦相似性計(jì)算方法結(jié)合,提出基于位置上下文信息的相似性計(jì)算方法,并利用這種相似性計(jì)算方法計(jì)算用戶之間的相似性,進(jìn)而尋找到目標(biāo)用戶的最近鄰居集,最后通過最近鄰居集中用戶的興趣愛好項(xiàng)為目標(biāo)用戶提供推薦。針對(duì)移動(dòng)互聯(lián)網(wǎng)環(huán)境下電子商務(wù)平臺(tái)產(chǎn)生龐大數(shù)據(jù)量的問題,對(duì)傳統(tǒng)協(xié)同過濾推薦算法造成嚴(yán)重的負(fù)載影響,本文提出了一種基于并行化迭代式k-medoids聚類的協(xié)同過濾推薦算法(CFPKM,A Collaborative Filtering Algorithm Based on Parallelized k-medoids Clustering)。該算法使用改進(jìn)的并行化k-medoids聚類算法,預(yù)先對(duì)推薦算法中的用戶-項(xiàng)目評(píng)分矩陣進(jìn)行聚類分析,將用戶分為k類。然后,計(jì)算目標(biāo)用戶與所屬類中的其他用戶之間的相似性,通過縮小數(shù)據(jù)空間來提高整個(gè)推薦算法的準(zhǔn)確度和推薦速度。最后,本文通過對(duì)提出算法的實(shí)驗(yàn)仿真,以及與傳統(tǒng)的協(xié)同過濾推薦算法進(jìn)行比較分析,證明了在考慮用戶位置信息以及利用聚類方式預(yù)先處理數(shù)據(jù)的情況下,進(jìn)一步提高了推薦算法的有效性和準(zhǔn)確性。
[Abstract]:With the continuous development of the Internet and the change of people's consumption concept, e-commerce platform has become the most important way of shopping. In this case, personalized recommendation system has become the main way to help users quickly find favorite products, but also become the main means to help the platform to sell goods. In the recommendation system, collaborative filtering recommendation algorithm is the most widely used. The main idea of the algorithm is to find the similar neighbor set of the target user, and recommend the user preference of the neighbor set to the target user as a recommendation item. However, with the rapid development of mobile Internet, the traditional collaborative filtering recommendation algorithm is facing more challenges. First, in the mobile Internet environment, e-commerce platform can obtain more information about users, such as user location context information. This information has a great impact on computing the similarity between users in the recommendation algorithm. Secondly, the mobile Internet brings a larger amount of data to the recommendation algorithm. Under big data environment, the real-time recommendation of the recommendation algorithm is difficult to be effectively guaranteed. In view of the two problems faced by the recommendation algorithm in the mobile Internet environment, this paper makes a thorough study and analysis, and puts forward the corresponding solution. In order to solve the problem of the influence of user location context information on the similarity calculation between users in the recommendation algorithm, this paper proposes a collaborative filtering recommendation algorithm based on location context information (CFLC, A Collaborative Filtering Algorithm Based on Location context). The algorithm combines the user location information with the modified cosine similarity calculation method, and proposes a similarity calculation method based on location context information. The similarity calculation method is used to calculate the similarity between the users, and then the nearest neighbor set of the target user is found. Finally, the user's interests and hobbies in the nearest neighbor set are recommended to the target user. In order to solve the problem of large amount of data generated by e-commerce platform in the mobile Internet environment, the traditional collaborative filtering recommendation algorithm is seriously affected by the load. In this paper, a collaborative filtering recommendation algorithm based on parallel iterative k-medoids clustering is proposed. A Collaborative Filtering Algorithm Based on Parallelized k-medoids clustering is proposed. The improved parallelized k-medoids clustering algorithm is used to cluster the user-item scoring matrix in the recommendation algorithm and the users are divided into k-class. Then, the similarity between the target user and other users in the class is calculated, and the accuracy and speed of the entire recommendation algorithm are improved by reducing the data space. Finally, through the experimental simulation of the proposed algorithm and the comparative analysis with the traditional collaborative filtering recommendation algorithm, it is proved that in the case of considering the user location information and using clustering to pre-process the data, The effectiveness and accuracy of the recommendation algorithm are further improved.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李穎基,彭宏,鄭啟倫,曾煒;自動(dòng)分層推薦算法[J];計(jì)算機(jī)應(yīng)用;2002年11期

2 徐義峰;徐云青;劉曉平;;一種基于時(shí)間序列性的推薦算法[J];計(jì)算機(jī)系統(tǒng)應(yīng)用;2006年10期

3 余小鵬;;一種基于多層關(guān)聯(lián)規(guī)則的推薦算法研究[J];計(jì)算機(jī)應(yīng)用;2007年06期

4 張海玉;劉志都;楊彩;賈松浩;;基于頁面聚類的推薦算法的改進(jìn)[J];計(jì)算機(jī)應(yīng)用與軟件;2008年09期

5 張立燕;;一種基于用戶事務(wù)模式的推薦算法[J];福建電腦;2009年03期

6 王晗;夏自謙;;基于蟻群算法和瀏覽路徑的推薦算法研究[J];中國科技信息;2009年07期

7 周珊丹;周興社;王海鵬;倪紅波;張桂英;苗強(qiáng);;智能博物館環(huán)境下的個(gè)性化推薦算法[J];計(jì)算機(jī)工程與應(yīng)用;2010年19期

8 王文;;個(gè)性化推薦算法研究[J];電腦知識(shí)與技術(shù);2010年16期

9 張愷;秦亮曦;寧朝波;李文閣;;改進(jìn)評(píng)價(jià)估計(jì)的混合推薦算法研究[J];微計(jì)算機(jī)信息;2010年36期

10 夏秀峰;代沁;叢麗暉;;用戶顯意識(shí)下的多重態(tài)度個(gè)性化推薦算法[J];計(jì)算機(jī)工程與應(yīng)用;2011年16期

相關(guān)會(huì)議論文 前10條

1 王韜丞;羅喜軍;杜小勇;;基于層次的推薦:一種新的個(gè)性化推薦算法[A];第二十四屆中國數(shù)據(jù)庫學(xué)術(shù)會(huì)議論文集（技術(shù)報(bào)告篇）[C];2007年

2 唐燦;;基于模糊用戶心理模式的個(gè)性化推薦算法[A];2008年計(jì)算機(jī)應(yīng)用技術(shù)交流會(huì)論文集[C];2008年

3 秦國;杜小勇;;基于用戶層次信息的協(xié)同推薦算法[A];第二十一屆中國數(shù)據(jù)庫學(xué)術(shù)會(huì)議論文集（技術(shù)報(bào)告篇）[C];2004年

4 周玉妮;鄭會(huì)頌;;基于瀏覽路徑選擇的蟻群推薦算法:用于移動(dòng)商務(wù)個(gè)性化推薦系統(tǒng)[A];社會(huì)經(jīng)濟(jì)發(fā)展轉(zhuǎn)型與系統(tǒng)工程——中國系統(tǒng)工程學(xué)會(huì)第17屆學(xué)術(shù)年會(huì)論文集[C];2012年

5 蘇日啟;胡皓;汪秉宏;;基于網(wǎng)絡(luò)的含時(shí)推薦算法[A];第五屆全國復(fù)雜網(wǎng)絡(luò)學(xué)術(shù)會(huì)議論文（摘要）匯集[C];2009年

6 梁莘q，

本文編號(hào)：1846420