【正文】 返回主頁(yè)，左上角每一個(gè)系統(tǒng)用戶都可以對(duì)個(gè)人信息進(jìn)行管理：　　　為了方便習(xí)題推薦系統(tǒng)內(nèi)部用戶的管理，只有系統(tǒng)管理員可以對(duì)所有用戶進(jìn)行增加，刪除，修改，查詢，支持線上利用模板導(dǎo)入、導(dǎo)出用戶到Excel。Struts作為系統(tǒng)的整體基礎(chǔ)架構(gòu)，負(fù)責(zé)MVC的分離，在Struts框架的模型部分，控制業(yè)務(wù)跳轉(zhuǎn)，利用Hibernate框架對(duì)持久層提供支持。示例代碼：在持久層中，則依賴于Hibernate的對(duì)象化映射和數(shù)據(jù)庫(kù)交互，處理DAO組件請(qǐng)求的數(shù)據(jù)，并返回處理結(jié)果。(4)服務(wù)器硬件環(huán)境：處理器:i5處理器內(nèi)存：16GB 內(nèi)存硬盤：300GB 硬盤(5)服務(wù)器軟件環(huán)境操作系統(tǒng)：window 8Java：Apache 服務(wù)器：5總結(jié)大學(xué)時(shí)光即將結(jié)束，畢業(yè)設(shè)計(jì)是大學(xué)的最后一次作品，是步入社會(huì)參與項(xiàng)目規(guī)劃建設(shè)極好的演練，是四年學(xué)習(xí)生活的總結(jié)和提高，和做真實(shí)項(xiàng)目開(kāi)發(fā)工作相似，必須要有嚴(yán)謹(jǐn)和實(shí)事求是的科學(xué)態(tài)度。在這個(gè)過(guò)程中，老師的的精心指導(dǎo)、與同學(xué)的交流、在圖書(shū)館查找資料、系統(tǒng)的完善，每一個(gè)過(guò)程都是對(duì)自己能力的一次檢驗(yàn)和充實(shí)。但是這次畢業(yè)設(shè)計(jì)的過(guò)程中同樣暴露出自己相關(guān)專業(yè)基礎(chǔ)的仍然有很多需改進(jìn)之處。通過(guò)這樣的一個(gè)自己從開(kāi)始到結(jié)束全程自己參與的設(shè)計(jì)來(lái)說(shuō)對(duì)知識(shí)的了解和掌握是純理論的學(xué)習(xí)遠(yuǎn)遠(yuǎn)達(dá)不到的效果。雖然之前對(duì)學(xué)校OJ系統(tǒng)不怎么了解，但是通過(guò)知道老師的給我的資料和自己找相關(guān)的網(wǎng)上資源對(duì)OJ系統(tǒng)有了一定的了解。最后一周是對(duì)以前的資料和設(shè)計(jì)思路進(jìn)行整理。在這個(gè)期間，如果沒(méi)有老師的細(xì)心指導(dǎo)，而是自己一個(gè)人獨(dú)自做設(shè)計(jì)，也不與任何人交流，這是完全不行的，設(shè)計(jì)其實(shí)也是一個(gè)團(tuán)隊(duì)的工作，如果不能與他人進(jìn)行良好的溝通與交流，做出的設(shè)計(jì)也只是只有自己一個(gè)人會(huì)欣賞，而別人卻不一定會(huì)認(rèn)可你的設(shè)計(jì)，所以在這次畢業(yè)設(shè)計(jì)過(guò)程中，我懂得了一個(gè)道理，團(tuán)結(jié)就是力量，多溝通與交流，吸取別人的建議，工作才會(huì)更加的順利。在此感謝我的畢業(yè)設(shè)計(jì)指導(dǎo)老師包云霞，謝謝您在畢業(yè)設(shè)計(jì)期間對(duì)我的指導(dǎo)。Ghaznavi Ghoushchi. Personalized remendation of learning material using sequential pattern mining and attribute based collaborative filtering, Volume 19 Issue 4, December 2014Pages 713735 Koper, R. (2008). Personal remender systems for learners in lifelong learning: requirements, techniques and model. International Journal of Learning Technology, 3(4), 404–423.[7]Felfernig, A., Friedrich G., SchmidtThieme, L., (2007). Introduction to the IEEE Intelligent Systems Special Issue: Remender Systems, 22(3) 18–21致謝辭時(shí)光飛逝，歲月如梭。本論文是在我的導(dǎo)師悉心指導(dǎo)下完成的。老師嚴(yán)謹(jǐn)求實(shí)的學(xué)術(shù)態(tài)度、不斷創(chuàng)新的研究作風(fēng)和誨人不倦的導(dǎo)師風(fēng)范讓我受益匪淺，為師和為人之道也是我今后學(xué)習(xí)和工作的榜樣。Apriori algorithm。 MultiattributeWith growth of many online learning systems, a huge amount of elearning materials have been generated which are highly heterogeneous and in various media formats (Chen et al. 2012). Therefore, in this situation, it is quite difficult to find suitable learning materials based on learner’s preference. The task of delivering personalized learning material is often framed in terms of a remendation task in which a system remends items to an active user (Mobasher 2007). Therefore, remender systems have been used for elearning environments to remend useful materials to users. These systems address information overload and make a personal learning environment (PLE) for users. The motivation for any remender system is to assure an efficient use of available materials. Using this approach, we can improve a personal learning path according to pedagogical issues and available material.In the recent years, remender system is being deployed in more and more emerce entities to best express and acmodate customer’s interests. According to the strategies applied, they can be divided into three major categories: contentbased, collaborative, and hybrid remendation (Adomavicius and Tuzhilin 2005). Contentbased remendation is derived from Information Retrieval. A contentbased remendation algorithm identifies and extracts features of items and user and then builds a matching model for them. Remendations are made based on parison of user’s preference and item’s features. On the other hand, the main idea of collaborative filtering is grouping likeminded users together. These systems are also called cliquebased systems. It is assumed that users who had similar choices before will make the same selection in the future. Collaborative remender systems give users suggestion by observing the neighbor of the user. Hybrid remendation mechanisms attempt to deal with some of limitation and overe drawbacks of pure contentbased approach and pure collaborative approach by bining the two approaches.There are several drawbacks when applying existing remendation algorithms to elearning environments directly:Since the learning process is repeatable and periodic, there are some intrinsic orders for learning material in users’ learning processes that can present material access patterns. This information can reflect the learner’s latent preference. But, most of existing remendation systems don’t use this information. To implement a sequential pattern based remendation, the new algorithms are presented in this research. Some of traditional remendation algorithms only use learners’ rating for remendation and don’t consider attributes of learners and learning materials. To model multipreference of learner this research takes into account multidimensionalattribute of materials and learners’ rating matrix in the unified model. The learners’ preferences will be changing dynamically. Therefore, to make good remendation in time when learners’ current interests are changing, a remendation algorithm must trace learner behaviour to propose dynamic remendation. Thus, this research implements a dynamic approach for producing remendations in the multidimensional attributebased CF. According to the described drawbacks, this paper proposes a new material remender system framework and relevant remendation algorithms for elearning environments. First, in the multidimensional attributebased CF remendation approach, to reflect learner’s plete spectrum of interests, Leaner Preference Tree (LPT) is introduced to consider multidimensionalattributes of materials, learner’s rating simultaneously. Truly, Leaner Preference Tree is built based on target learner’s historical access records and multidimensionalattributes of materials. Then, a new similarity measure that can take into account the information of LPTs for calculating similarity between learners is introduced. In the sequential pattern based remendation approach, to discover the latent patterns of accessed materials and give remendation, the weighted association rules (Apriori algorithm) and PrefixSpan algorithm are implemented. The results of two approaches are bined to create final remendations.The rest of this paper is　 Literature survey，In Literature survey section, the previous related works on elearning material remender systems are discussed.Learning materials have grew either offline or online in educational organizations. So, it is diffi