發(fā)布時(shí)間：2018-01-01 12:07

  本文關(guān)鍵詞：基于協(xié)同過(guò)濾的個(gè)性化推薦算法研究及系統(tǒng)實(shí)現(xiàn)　出處：《西南交通大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 推薦系統(tǒng) 協(xié)同過(guò)濾 基于用戶(hù)的協(xié)同過(guò)濾算法 k-means聚類(lèi)算法 用戶(hù)相似度 懲罰因子 推薦系統(tǒng)實(shí)現(xiàn)

【摘要】：隨著互聯(lián)網(wǎng)的飛速發(fā)展,信息呈現(xiàn)幾何級(jí)的增長(zhǎng),海量數(shù)據(jù)問(wèn)題日趨嚴(yán)重,用戶(hù)快速獲得自己感興趣的信息變得相當(dāng)困難。加之移動(dòng)設(shè)備的普及,用戶(hù)更愿意在移動(dòng)設(shè)備上獲取信息。在這樣的情況下,個(gè)性化推薦系統(tǒng)應(yīng)運(yùn)而生。本文在實(shí)際需求的驅(qū)動(dòng)下,首先了解推薦系統(tǒng)的理論知識(shí),學(xué)習(xí)相關(guān)推薦算法,然后重點(diǎn)研究了協(xié)同過(guò)濾推薦算法和k-means聚類(lèi)算法,并對(duì)協(xié)同過(guò)濾算法進(jìn)行改進(jìn)。最后設(shè)計(jì)和實(shí)現(xiàn)了文章推薦子系統(tǒng)。本文做的工作主要有以下幾個(gè)方面:1.分析了熱門(mén)對(duì)象可能對(duì)推薦算法中用戶(hù)相似度的影響,提出在計(jì)算用戶(hù)相似度時(shí)加入懲罰因子,以降低熱門(mén)對(duì)象對(duì)用戶(hù)相似度的影響。通過(guò)實(shí)驗(yàn)驗(yàn)證,提出的算法的準(zhǔn)確率和召回率都有所提高。2.對(duì)于協(xié)同過(guò)濾算法的時(shí)間瓶頸及擴(kuò)展問(wèn)題,提出了采用聚類(lèi)算法對(duì)協(xié)同過(guò)濾算法進(jìn)行改進(jìn)。在聚類(lèi)時(shí)不單單使用用戶(hù)的評(píng)分信息,而且挖掘了用戶(hù)評(píng)分對(duì)象的特征信息,通過(guò)這兩部分信息為用戶(hù)偏好進(jìn)行建模,進(jìn)而聚類(lèi)。通過(guò)實(shí)驗(yàn)驗(yàn)證,改進(jìn)后的算法的推薦效率與預(yù)測(cè)準(zhǔn)確率都有所提升。在此基礎(chǔ)上,綜合懲罰因子與用戶(hù)聚類(lèi)兩種策略來(lái)改進(jìn)基于用戶(hù)的協(xié)同過(guò)濾算法。通過(guò)實(shí)驗(yàn)驗(yàn)證,綜合改進(jìn)比任意的單一改進(jìn)在推薦效率與推薦質(zhì)量都要優(yōu)越。3.根據(jù)某公司業(yè)務(wù)的特定手機(jī)應(yīng)用場(chǎng)景和具體需求,應(yīng)用提出的協(xié)同過(guò)濾算法設(shè)計(jì)并實(shí)現(xiàn)了文章推薦子系統(tǒng)。該子系統(tǒng)主要包含用戶(hù)日志收集與處理模塊、用戶(hù)與推薦對(duì)象建模模塊、推薦模塊和推薦列表展示模塊,并且對(duì)這四個(gè)模塊進(jìn)行設(shè)計(jì)與實(shí)現(xiàn)。
[Abstract]:With the rapid development of Internet, information is growing exponentially, massive data is becoming a serious problem, users quickly get the interesting information becomes quite difficult. Coupled with the popularity of mobile devices, users prefer to get information on a mobile device. In this case, the personalized recommendation system came into being. Based on the actual needs of the driver first, understand the theory of knowledge recommendation system, learning recommendation algorithm, and then focus on the collaborative filtering algorithm and K-means clustering algorithm and the collaborative filtering algorithm was improved. The design and implementation of the recommended subsystem. The main work of this paper is as follows: 1. analysis of hot objects may affect the user recommendation algorithm in similarity, is proposed by adding a penalty factor in calculating the similarity of users, in order to reduce the degree of similarity of users hot. Ring. Through the experiment, the accuracy of the proposed algorithm and the recall rate has increased to.2. and extended the time bottleneck problem in collaborative filtering algorithm, the clustering algorithm of collaborative filtering algorithm is improved. Not only the user clustering score information and mining feature information of user rating objects. Through this two part information for user preference modeling and clustering. Through the experiment, the recommended efficiency and improved prediction accuracy are improved. On this basis, the comprehensive penalty factor and user clustering two strategies to improve user based collaborative filtering algorithm. Through the experiment, comprehensive improvement than single improvement in any recommendation efficiency and quality of recommendation can be superior to.3. according to specific application scenarios of a mobile phone business and the specific needs, using the proposed collaborative filtering algorithm. The sub recommendation system is implemented and implemented. The subsystem mainly includes user log collection and processing module, user and recommendation object modeling module, recommendation module and recommendation list display module, and designs and implements these four modules.

【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.3

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