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1、附录1 英文原文Personalized recommendation of learning material using sequential pattern mining and attribute based collaborative filteringAbstractMaterial recommender system is a significant part of e-learning systems for personalization and recommendation of appropriate materials to learners. However, in
2、 the existing recommendation algorithms, dynamic interests and multi-preference of learners and multidimensional-attribute of materials are not fully considered simul-taneously. Moreover, these algorithms cannot effectively use the learners historical sequential patterns of material accessing in rec
3、ommendation. For addressing these problems and improving the accuracy and quality of recommendation, a new material recommender system framework based on sequential pattern mining and multidimensional attribute-based collaborative filtering (CF) is proposed. In the sequential pattern based approach,
4、 modified Apriori and PrefixSpan algorithms are implemented to discover latent patterns in accessing of materials and use them for recommendation. Leaner Preference Tree (LPT) is introduced to take into account multidimensional-attribute of materials, and learners rating and model dynamic and multi-
5、preference of learners in the multidimensional attribute-based CF ap-proach. Finally, the recommendation results of two approaches are combined using cascade, weighted and mixed methods. The proposed method outperforms the previous algorithms on the classification accuracy measures and the learners
6、real learning preference can be satisfied accurately according to the real-time up dated contextual information.Keywords: Personalized recommendation ;Apriori algorithm;. Learning material ;e-learning ; Dynamic preference ; Multi-attributeWith growth of many online learning systems, a huge amount of
7、 e-learning 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 learners preference. The task of delivering personalized learning material is oft
8、en framed in terms of a recommendation task in which a system recommends items to an active user (Mobasher 2007). Therefore, recom-mender systems have been used for e-learning environments to recommend useful materials to users. These systems address information overload and make a personal learning
9、 environment (PLE) for users. The motivation for any recommender 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, recommender system is being deployed
10、in more and more e-commerce entities to best express and accommodate customers interests. According to the strategies applied, they can be divided into three major categories: content-based, collaborative, and hybrid recommendation (Adomavicius and Tuzhilin 2005). Content-based recommendation is der
11、ived from Information Retrieval. A content-based recommendation algorithm identifies and extracts features of items and user and then builds a matching model for them. Recommendations are made based on comparison of users preference and items features. On the other hand, the main idea of collaborati
12、ve filtering is grouping like-minded users together. These systems are also called clique-based systems. It is assumed that users who had similar choices before will make the same selection in the future. Collaborative recommender systems give users suggestion by observing the neighbor of the user.
13、Hybrid recom-mendation mechanisms attempt to deal with some of limitation and overcome draw-backs of pure content-based approach and pure collaborative approach by combining the two approaches.There are several drawbacks when applying existing recommendation algorithms to e-learning environments dir
14、ectly: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 learners latent preference. But, most of existing recommendation systems dont use
15、 this information. To imple-ment a sequential pattern based recommendation, the new algorithms are pre-sented in this research. Some of traditional recommendation algorithms only use learners rating for recommendation and dont consider attributes of learners and learning materials. To model multi-pr
16、eference of learner this research takes into account multidimensional-attribute of materials and learners rating matrix in the unified model. The learners preferences will be changing dynamically. Therefore, to make good recommendation in time when learners current interests are changing, a recom-me
17、ndation algorithm must trace learner behaviour to propose dynamic recom-mendation. Thus, this research implements a dynamic approach for producing recommendations in the multidimensional attribute-based CF. According to the described drawbacks, this paper proposes a new material recom-mender system
18、framework and relevant recommendation algorithms for e-learning environments. First, in the multidimensional attribute-based CF recommendation approach, to reflect learners complete spectrum of interests, Leaner Preference Tree (LPT) is introduced to consider multidimensional-attributes of materials
19、, learn-ers rating simultaneously. Truly, Leaner Preference Tree is built based on target learners historical access records and multidimensional-attributes of materials. Then, a new similarity measure that can take into account the information of LPTs for calculating similarity between learners is
20、introduced. In the sequential pattern based recommendation approach, to discover the latent patterns of accessed materials and give recommendation, the weighted association rules (Apriori algorithm) and PrefixSpan algorithm are implemented. The results of two approaches are combined to create final
21、recommendations.The rest of this paper is Literature survey，In Literature survey section, the previous related works on e-learning material recommender systems are discussed.Learning materials have grew either offline or online in educational organizations. So, it is difficult for learners to discov
22、er the most appropriate materials according to keyword searching methods. The creation of the technology for personalized lifelong learning has been recognized as a Grand Challenge Problem by peak research bodies (Kay 2008). Therefore, recommender systems have been used for e-learning environ-ments
23、to recommend useful materials to users. The first recommender system was developed in the mid of 1990s (Felfernig et al. 2007). Many recommendation systems in various fields such as movies, music, news, commerce and medicine have been developed but few in education field (Drachsler et al. 2007). The
24、 Overview of the recommendation strategies and techniques with their usefulness for material recom-mendation have been presented in Table 1. We briefly survey some of important works and explain the drawbacks of them that can be addressed by our proposed approach.Content based filtering This techniq
25、ue suggests items similar to the ones that each user liked in the past taking into account the object content analysis that the user has evaluated in the past (Lops et al. 2011). As an example for e-learning application, Khribi et al. ( 2009) used learners recent navigation histories and similaritie
26、s and dissimilarities among the contents of the learning materials for online automatic recommendations. Clustering was proposed by Hammouda and Kamel ( 2006) to group learning documents based on their topics and similarities. In fact, the existing metrics in content based filtering only detect simi
27、larity between items that share the same attributes. Indeed, the basic process performed by a content-based recommender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (item), in order to recom-mend to th
28、e user new interesting items (Lops et al. 2011). This causes overspecialized recommendations that only include items very similar to those the user already knows. To avoid the overspecialization of content-based methods, researchers pro-posed new personalization strategies, such as collaborative fil
29、tering and hybrid approaches mixing both techniques.Collaborative filtering Majority of researchers used collaborative filtering based recommendation system. (Milicevic et al. 2010; Bobadilla et al. 2010). CF approaches used in e-learning environments focus on the correlations among users having sim
30、ilar interests (Marlin 2004; Sergio et al. 2005) and can be divided in to three categories that have been shown in Table 1. The collaborative e-learning field is strongly growing (Tan et al. 2008; Garca et al. 2009; Garca et al. 2011; Wang and Liao 2011), converting this area in an important receive
31、r of applications and generating numerous research papers. Collaborative filtering was used by Soonthornphisaj et al. ( 2006) for prediction the most suitable materials for the learner. At first, the weight between all users and the active learner is calculated by Pearson correlation. Then, the n us
32、ers that have the highest similarity to the active learner are selected as the neighborhoods. Finally, using the weight combination obtained from the neighborhood, the rating prediction is calculated. Bobadilla et al. ( 2009) used a new equation for incorporating the learners score obtained from a t
33、est into the calculations in collaborative filtering for materials prediction. Their experiment showed that the method obtained high item-prediction accuracy.Since in the e-learning environment learning materials are in a variety of multi-media formats including text, hypertext, image, video, audio
34、and slides, it is difficult to calculate content similarity of two items (Chen et al. 2012). In this sense, users preference information is a good indication for recommendation. Therefore, CF is more suitable in e-learning systems since it is completely independent of the intrinsic properties of the
35、 items being rated or recommended (Yu et al. 2011).Regardless of its success in many application domains, collaborative filtering has two serious drawbacks. First, its applicability and quality are limited by the so-called sparsity problem, which occurs when the available data are insufficient for i
36、dentifying similar users (Cotter and Smyth 2000). Therefore, many researches were run to alleviate sparsity problem using data mining techniques. For example, Romero et al. ( 2009) developed a specific Web mining tool for discovering suitable rules in recommender engine. Their objective was to recom
37、mend to a student the most appropriate links/WebPages to visit next. Second, it requires knowing many user profiles in order to elaborate accurate recommendations for a given user. Therfore, in some e-learning enviroment that number of learner is low, recommendation result has not adequate accuracy.
38、Hybrids To overcome drawbacks of these strategies, researchers used hybrid approaches for material recommendation. Combining several recommendation strat-egies can be expected to provide better results than either strategy alone. As examples for in e-learning environment, Liang et al. ( 2006) implem
39、ented the combination of content-based filtering and collaborative filtering to make personal-ized recommendations for a courseware selection module. The algorithm starts with user u entering some keywords on the portal of courseware management system. Next, the courseware recommendation module find
40、s within the same user interest group of user u the k courseware with the same or similar keywords that others choose. Garca et al. ( 2009) applied association rule mining to discover interesting information through students usage data in the form of IF-THEN recommendation rules and then used a coll
41、aborative recommender system to share and score the recommendation rules obtained by teachers with similar profiles along with other experts in education.An appropriate recommendation technique must be chosen according to pedagog-ical reasons. These pedagogical reasons are derived from specific dema
42、nds of lifelong learning (Drachsler et al. 2007). One way to implement pedagogical decisions into a recommender system is to use a variety of recommendation techniques in a recom-mendation strategy. The decision to change from one recommendation technique to another can be done according to pedagogi
43、cal reasons, derived from specific demands of lifelong learning (Drachsler et al. 2008). This paper uses two recommen-dation techniques based on explicit and implicit attributes of learner and materials.First technique integrates multi-dimensional attributes of materials, learners rating information
44、 using proposed learner preference tree. Our proposed framework can use this information simultaneously to model adaptive multi-preference of learner. According to the property of this technique, system can improve the accuracy and diversity of recommendation. The second technique integrates informa
45、tion about sequential latent pattern of accessed materials by learners. Using this information and applying sequential pattern mining algorithms help us to filter items according to common learning sequences.In summary, in order to improve the learning material recommendation efficiency, developing
46、a framework for integrating multidimentional-attributes of materials, learn-ers rating information and also latent patterns of material access is necessary. Most of researches only use some of this information in material recommendation process.附录2 中文原文利用序列模式树和基于内容过滤的个性化推荐摘要基于内容的推荐是电子学习系统中个性化推荐给学生合适
47、资料的的显著部分。但是，在现有采用的算法中，动态的兴趣和学习者的多偏好及物质的多维属性没有充分考虑。此外，这些算法不能有效地利用学习者访问材料的历史序列模式。为了解决这些问题，提高了推荐精度和推荐质量，基于序列模式挖掘和基于多维属性的协同过滤（CF）的推荐系统框架方案被提出。在序列模式为基础的方法，修改和先验的PrefixSpan算法实现的发现潜在的模式在材料访问和使用它们的建议。学习者偏好树（LPT）引入考虑到资源的多维属性，和学生的等级及动态模型和学习者在基于多维属性的建议。最后，两种方法的结果推荐使用级联组合，权重和混合的方法。该方法优于以前的算法，并且学习者的真正的学习偏好可以准确地根
48、据下文信息实时得到满足。关键词：个性化推荐,Apriori算法,在线学习,动态偏好,多属性对于许多在线学习系统，巨大的电子学习材料的量已经发生了很大变化，并在各种媒体格式（Chen等2012）的增长。因此，在这种情况下，是很难找到根据学习者的喜好适合自己的学习材料。提供个性化学习资源的任务往往是在框架中，系统建议项目活动的用户的推荐任务而言（2007年Mobasher）。因此，推荐系统已被用于电子学习环境，建议有用的资源给用户。这些系统解决信息过载，并为用户提供个人学习环境（PLE）。对于任何推荐系统的动机是为了确保有效地使用可用的材料。使用这种方法，我们可以根据教学问题和可用的材料提高了个人
49、学习计划。在最近几年，推荐系统被部署在越来越多的电子商务实体最好表示和容纳客户的利益。根据施加的战略，它们可分为三大类：基于内容，协作和混合建议（Adomavicius和Tuzhilin 2005）。基于内容的推荐是从信息检索的。基于内容的推荐算法识别并提取内容和用户的功能，然后建立一个匹配模型他们。推荐是根据用户的偏好和项目的特点比较的。在另一方面，协同过滤的主要思想是分组志趣相投的用户在一起。这些系统也被称为基于组别的系统。假定谁收到了类似的选择的用户将在将来的相同的选择。协同推荐系统通过观察用户的邻居给用户的建议。混合段中议书机制试图处理一些局限性，并通过这两种方法相结合克服了纯粹的基于内容的方法和纯净的协作方式绘制的难点。应用现有推荐算法为电子学习直接环境时有几个缺点：（1）、由于学习的过程是可重复和定期的，总会有适合学习者某种固有的以访问资料的顺序呈现的学习过程。这些信息可以反映出学习者的潜在偏好。但是，大多数现有的推荐系统中不使用这些信息。为了能够应用基于推荐的序列模式，新的算法在本文中提出。（2）、一些传统的推荐算法只使用学