本文選題：協(xié)同過濾 + 時間上下文信息�。� 參考：《電子科技大學(xué)》2015年碩士論文

【摘要】：隨著社會的計算機(jī)化,人們生產(chǎn)和收集數(shù)據(jù)的能力顯著增強。海量信息在給這個時代帶來機(jī)遇的同時也帶來了許多棘手的難題,那就是信息的篩選和過濾。推薦系統(tǒng)是一種智能的信息篩選工具:系統(tǒng)自動記錄并分析用戶的歷史行為數(shù)據(jù),然后將用戶最感興趣的信息呈現(xiàn)在用戶面前。自推薦系統(tǒng)問世以來,被廣泛地運用于在線電子商務(wù)系統(tǒng)中,同時也面臨許多問題,例如以往算法未將時間這一動態(tài)信息建模到推薦模型中,從而不能捕獲到與時間相關(guān)的各種重要規(guī)律,這稱為靜態(tài)推薦。為了解決這一問題,本文首先對推薦領(lǐng)域各類算法進(jìn)行深入分析;然后,重點探討了時間這一動態(tài)上下文信息對推薦結(jié)果的影響,并以協(xié)同過濾推薦算法為基礎(chǔ),將時間信息建模到該算法中,并給出具體的算法流程;最后結(jié)合移動設(shè)備特點,提出融合了移動設(shè)備特點的推薦方案并應(yīng)用于在線圖書銷售系統(tǒng)中。本文的主要研究工作如下:(1)對目前主流的推薦算法進(jìn)行深入研究,分析了各個算法的推薦機(jī)制以及優(yōu)缺點,并通過簡單實例對各算法進(jìn)行了描述。重點分析了協(xié)同過濾推薦,詳細(xì)描述了該類算法的執(zhí)行流程;(2)通過分析現(xiàn)實生活推薦機(jī)制中的時間規(guī)律以及Netflix數(shù)據(jù)集中的時間現(xiàn)象,論證了時間信息對推薦系統(tǒng)的重要性;(3)對時間信息進(jìn)行建模,提出了基于用戶影響度的算法(IOE-User CF)以及基于物品耦合度及流行度的算法(PC-Item CF),并給出具體的流程;然后利用Netflix數(shù)據(jù)集從MAE值、推薦的準(zhǔn)確率、召回率以及F1值等指標(biāo)對兩類算法的推薦質(zhì)量進(jìn)行評測。通過實驗得出:本文融合了時間信息的算法比以往算法具有更好的推薦準(zhǔn)確度;(4)最后,利用本文的算法思路,同時結(jié)合移動設(shè)備能方便地獲取用戶通訊錄這一優(yōu)點,建立融合移動設(shè)備特點的動態(tài)推薦模型。(5)為了論證本文的推薦模型具有一定的使用價值,最后設(shè)計一個簡單的在線圖書銷售系統(tǒng),并將上述模型應(yīng)用該系統(tǒng)中。
[Abstract]:With the computerization of society, people's ability to produce and collect data has increased significantly. Mass information not only brings opportunities to this era, but also brings a lot of difficult problems, that is, information screening and filtering. Recommendation system is an intelligent information filtering tool: the system automatically records and analyzes the user's historical behavior data, and then presents the most interesting information to the user. Since the advent of the recommendation system, it has been widely used in the online e-commerce system, but also faces many problems, such as the previous algorithm did not model this dynamic information of time into the recommendation model. This does not capture the time-related important laws, this is called static recommendation. In order to solve this problem, this paper firstly analyzes all kinds of algorithms in recommendation field, and then discusses the influence of time, a dynamic context information, on the recommendation results, based on collaborative filtering recommendation algorithm. The time information is modeled in the algorithm, and the specific algorithm flow is given. Finally, combining the characteristics of mobile devices, a recommendation scheme is proposed and applied to the online book sales system. The main research work of this paper is as follows: (1) deeply study the current mainstream recommendation algorithms, analyze the recommendation mechanism, advantages and disadvantages of each algorithm, and describe each algorithm through a simple example. The collaborative filtering recommendation is analyzed in detail. (2) by analyzing the time rule in the real life recommendation mechanism and the time phenomenon in Netflix data set, The importance of time information to recommendation system is demonstrated. (3) the time information is modeled, and the algorithm based on user's influence degree (IOE-User CF) and the algorithm based on item coupling and popularity (PC-Item CF) are proposed, and the concrete flow is given. Then the recommendation quality of the two algorithms is evaluated by Netflix data set from the mae value, recommendation accuracy rate, recall rate and F1 value. The experimental results show that the proposed algorithm has better recommendation accuracy than the previous algorithms. (4) finally, using the algorithm of this paper, combined with the advantages of mobile devices can easily obtain the user address book. In order to prove that the recommendation model in this paper has some practical value, a simple online book sales system is designed and applied in this system. (5) A dynamic recommendation model combining the characteristics of mobile devices is established. (5) in order to prove that the recommendation model of this paper has some practical value, a simple online book sales system is designed.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F724.6;TP391.3

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相關(guān)期刊論文 前1條

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本文編號：2081098