本文選題：推薦系統(tǒng)　切入點：仿真平臺　出處：《電子科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：互聯(lián)網(wǎng)技術(shù)的迅速發(fā)展導(dǎo)致了信息過載問題。面對信息過載問題,雖然有相關(guān)應(yīng)用如檢索系統(tǒng)和搜索引擎可以幫助人們更精確的找到所需要的信息。在某些應(yīng)用領(lǐng)域,比如電影、音樂、購物以及交友平臺等,用戶一般不能很好的提出很好的檢索需求,在某些情況下,由用戶自己提出來的搜索關(guān)鍵字獲得的結(jié)果對于用戶而言可能是無價值的。推薦系統(tǒng)的產(chǎn)生解決了如上的問題,它通過研究用戶的歷史記錄、用戶的社會化信息以及對應(yīng)分類數(shù)據(jù)的屬性信息,將用戶的信息建�；蛘叻诸悢�(shù)據(jù)資源建模,通過某種可信賴方式的方式將用戶潛在感興趣的數(shù)據(jù)資源推薦給用戶。這些推薦系統(tǒng)都能在某種程度上提高用戶尋找特定信息的效率,而且有助于特定內(nèi)容信息的有效傳播。但這些推薦系統(tǒng)存在的共同問題就是,如何讓推薦的結(jié)果更好,推薦的效率更高,用戶的滿意度更高。由此需要一個通用的工具對這些推薦算法進行評價,讓算法的提供者能更好的獲得算法的反饋結(jié)果,更好的取修正自己的算法,讓他們能夠?qū)Ξ悩?gòu)數(shù)據(jù)的輸入以及采樣有一個相對通用的接口可以使用。本文提出一種設(shè)計方案,實現(xiàn)一個推薦系統(tǒng)綜合仿真平臺的評估框架,能夠模擬算法提供的各種輸入數(shù)據(jù)集輸入,并且對這些數(shù)據(jù)集進行統(tǒng)一的管理與存儲,并且對其進行數(shù)據(jù)加工,對于不同的算法實現(xiàn)熱插拔,算法可以遵守對應(yīng)的規(guī)整進行不同的配置,來使用之前系統(tǒng)所統(tǒng)一管理的數(shù)據(jù)集,從而進行離線實驗,再根據(jù)獲得的反饋結(jié)果進行結(jié)果評估,讓算法提供者對于算法能夠有更好的改進。本文著眼于推薦系統(tǒng)綜合仿真平臺評估框架中數(shù)據(jù)集的統(tǒng)一管理、統(tǒng)一收集,多樣化的數(shù)據(jù)采樣,數(shù)據(jù)交叉驗證方式、數(shù)據(jù)可視化、數(shù)據(jù)特征展示以及推薦結(jié)果評估,對于算法的具體實現(xiàn)以及推薦結(jié)果返回,并不是本文討論的重點,本文將以一種簡單的例子表示用戶可自由配置的算法執(zhí)行體。本文的思路:首先對推薦系統(tǒng)的發(fā)展現(xiàn)狀進行了總結(jié),研究了構(gòu)建一個推薦系統(tǒng)綜合仿真平臺需要使用到的相關(guān)技術(shù),討論了常用的數(shù)據(jù)處理技術(shù),聚類算法,數(shù)據(jù)可視化以及分布式存儲等技術(shù)。然后討論了評估框架的具體需求,其次根據(jù)這些需求分別針對框架中數(shù)據(jù)實體容器,數(shù)據(jù)特征展示工具以及仿真評估模塊的設(shè)計,選擇采用django這種MVC框架來作為用戶交互界面,最后實現(xiàn)這種多樣化數(shù)據(jù)輸入的綜合仿真評估平臺。
[Abstract]:The rapid development of Internet technology has led to the problem of information overload. Faced with the problem of information overload, although there are related applications such as retrieval systems and search engines to help people find the information they need more accurately. For example, movies, music, shopping, dating platforms, etc., users generally can not put forward very good retrieval requirements, in some cases, The results obtained by the user's own search keywords may be of no value to the user. The generation of the recommendation system solves the above problem by studying the user's history. The social information of the user and the attribute information corresponding to the classified data are modeled as the information of the user or the classified data resource. Recommend data resources of potential interest to users in a way that can be trusted. These recommendation systems can improve the efficiency of searching for specific information to some extent. But the common problem with these recommendation systems is how to make recommendations better and more efficient. User satisfaction is higher. A common tool is needed to evaluate these recommended algorithms, so that the provider of the algorithm can better obtain the feedback results of the algorithm, and better take and modify their own algorithm. So that they can use a relatively common interface for heterogeneous data input and sampling. This paper proposes a design scheme to implement an evaluation framework for a recommendation system integrated simulation platform. It can simulate the input of various input data set provided by the algorithm, and manage and store these data sets uniformly, and process the data. For different algorithms, it can be hot-swapped. The algorithm can be configured according to the corresponding rules to make use of the data set managed by the previous system, and then carry out off-line experiments, and then evaluate the results according to the feedback results obtained. This paper focuses on the unified management of data sets, unified collection, diverse data sampling, data cross-validation methods, data visualization in the evaluation framework of the integrated simulation platform of recommendation system. The presentation of data features and the evaluation of the recommended results are not the key points of this paper for the implementation of the algorithm and the return of the recommended results. This paper will use a simple example to represent the user freely configurable algorithm executor. The idea of this paper: first of all, the development of the recommendation system is summarized. This paper studies the relevant technologies needed to build a recommendation system integrated simulation platform, discusses the commonly used technologies of data processing, clustering algorithm, data visualization and distributed storage, and then discusses the specific requirements of the evaluation framework. Secondly, according to these requirements, the design of data entity container, data feature display tool and simulation evaluation module in the framework is carried out, and the MVC framework of django is chosen as the user interface. Finally, the comprehensive simulation and evaluation platform for the diversified data input is implemented.
【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】：TP391.3
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本文編號：1634662