Fuzzy ontologies-based user profiles applied to enhance e-learning activities

Nowadays, the impact of technological developments on improving human activities is becoming more evident. In e-learning, this situation is no different. There are common to use systems that assist the daily activities of students and teachers. Typically, e-learning recommender systems are focused on students; however, teachers can also benefit from these type of tools. A recommender system can propose actions and resources that facilitate teaching activities like structuring learning strategies. In any case, a complete user’s representation is required. This paper shows how a fuzzy ontology can be used to represent user profiles into a recommender engine and enhances the user’s activities into e-learning environments. A fuzzy ontology is an extension of domain ontologies for solving the problems of uncertainty in sharing and reusing knowledge on the Semantic Web. The user profile is built from learning objects published by the user himself into a learning object repository. The initial experiment confirms that the automatically obtained fuzzy ontology is a good representation of the user’s preferences. The experiment results also indicate that the presented approach is useful and warrants further research in recommending and retrieval information.     

Utilizing learning process to improve recommender system for group learning support

With the rapid increasing of learning materials and learning objects in e-learning, the need for recommender system has also become more and more imperative. Although, the traditional recommendation system has achieved great success in many domains, it is not suitable to support e-learning recommender system because the approach in e-learning is hybrid and it is obtained mainly by two mechanisms: the learners’ learning processes and the analysis of social interaction. Therefore, this study proposes a flexible recommendation approach to satisfy this demand. The recommendation is designed based on a multidimensional recommendation model. Furthermore, we use Markov Chain Model to divide the group learners into advanced learners and beginner learners by using the learners’ learning activities and learning processes so that we can correctly estimate the rating which also include learners’ social interaction. The experimental result shows that the proposed system can give a more satisfying and qualified recommendation.     

e‐Learning recommender system for a group of learners based on the unified learner profile approach

In the age of information explosion, e‐learning recommender systems (eL_RSs) have emerged as effective information filtering techniques that attempt to provide the most appropriate learning resources for learners while using e‐learning systems. These learners are differentiated on the basis of their learning styles, goals, knowledge levels and others. Several attempts have been made in the past to design eL_RSs to recommend resources to individuals; however, an investigation of recommendations to a group of learners in e‐learning is still in its infancy. In this paper, we focus on the problem of recommending resources to a group of learners rather than to an individual. The major challenge in group recommendation is how to merge the individual preferences of different learners that form a group and extract a pseudo unified learner profile (ULP) that closely reflects the preferences of all learners. Firstly, we propose a profile merging scheme for the ULP by utilizing learning styles, knowledge levels and ratings of learners in a group. Thereafter, a collaborative approach is proposed based on the ULP for effective group recommendations. Experimental results are presented to demonstrate the effectiveness of the proposed group recommendation strategy for e‐learning.     

基于本体的知识库模型研究

本体作为一种能在语义和知识层次上描述信息系统的概念模型建模工具,近年来在计算机的许多领域得到了广泛的应用。其中,基于本体的知识构建是一个复杂的系统工程。就目前的研究状况来说,主要是解决如何构建知识模型的问题。本文运用本体知识模型相关的理论,在分析了本体论、知识库和知识库系统概念的基础上探讨了本体论在知识库系统中的应用和方法,提出了一种基于本体的知识模型,并阐述了基于本体的知识库构建方法。     

User‐centred design and educational data mining support during the recommendations elicitation process in social online learning environments

Social online learning environments provide new recommendation opportunities to meet users' needs. However, current educational recommender systems do not usually take advantage of these opportunities. To progress on this issue, we have proposed a knowledge engineering approach based on human–computer interaction (i.e. user‐centred design as defined by the standard ISO 9241‐210:2010) and artificial intelligence techniques (i.e. data mining) that involve educators in the process of eliciting educational oriented recommendations. To date, this approach differs from most recommenders in education in focusing on identifying relevant actions to be recommended on e‐learning services from a user‐centric perspective, thus widening the range of recommendation types. This approach has been used to identify 32 recommendations that consider several types of actions, which focus on promoting active participation of learners and on strengthening the sharing of experiences among peers through the usage of the social services provided by the learning environment. The paper describes where data mining techniques have been applied to complement the user‐centred design methods to produce social oriented recommendations in online learning environments.     

Protus 2.0: Ontology-based semantic recommendation in programming tutoring system

With the development of the Semantic web the use of ontologies as a formalism to describe knowledge and information in a way that can be shared on the web is becoming common. The explicit conceptualization of system components in a form of ontology facilitates knowledge sharing, knowledge reuse, communication and collaboration and construction of knowledge rich and intensive systems. Semantic web provides huge potential and opportunities for developing the next generation of e-learning systems. In previous work, we presented tutoring system named Protus (PRogramming TUtoring System) that is used for learning the essence of Java programming language. It uses principles of learning style identification and content recommendation for course personalization. This paper presents new approach to perform effective personalization highly based on Semantic web technologies performed in new version of the system, named Protus 2.0. This comprises the use of an ontology and adaptation rules for knowledge representation and inference engines for reasoning. Functionality, structure and implementation of a Protus 2.0 ontology as well as syntax of SWRL rules implemented for on-the-fly personalization will be presented in this paper.     

E-Learning personalization based on hybrid recommendation strategy and learning style identification

Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper, we describe a recommendation module of a programming tutoring system - Protus, which can automatically adapt to the interests and knowledge levels of learners. This system recognizes different patterns of learning style and learners’ habits through testing the learning styles of learners and mining their server logs. Firstly, it processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the learners through mining the frequent sequences by the AprioriAll algorithm. Finally, this system completes personalized recommendation of the learning content according to the ratings of these frequent sequences, provided by the Protus system. Some experiments were carried out with two real groups of learners: the experimental and the control group. Learners of the control group learned in a normal way and did not receive any recommendation or guidance through the course, while the students of the experimental group were required to use the Protus system. The results show suitability of using this recommendation model, in order to suggest online learning activities to learners based on their learning style, knowledge and preferences.     

Human computation for constraint-based recommenders

PeopleViews is a Human Computation based environment for the construction of constraint-based recommenders. Constraint-based recommender systems support the handling of complex items where constraints (e.g., between user requirements and item properties) can be taken into account. When applying such systems, users are articulating their requirements and the recommender identifies solutions on the basis of the constraints in a recommendation knowledge base. In this paper, we provide an overview of the PeopleViews environment and show how recommendation knowledge can be collected from users of the environment on the basis of micro-tasks. We also show how PeopleViews exploits this knowledge for automatically generating recommendation knowledge bases. In this context, we compare the prediction quality of the recommendation approaches integrated in PeopleViews using a DSLR camera dataset.     

知识驱动的推荐系统：现状与展望

个性化推荐系统能够根据用户的个性化偏好和需要,自动、快速、精准地为用户提供其所需的互联网资源,已成为当今大数据时代应用最广泛的信息检索系统,具有巨大的商业应用价值。近年来,随着互联网海量数据的激增,人工智能技术的快速发展与普及,以知识图谱为代表的大数据知识工程日益受到学界和业界的高度关注,也有力地推动推荐系统和个性化推荐技术也迈入到知识驱动与赋能的发展阶段。将知识图谱中蕴含的丰富知识作为有用的辅助信息引入推荐系统,不仅能够有效应对数据稀疏、语义失配等传统推荐系统难以避免的问题,还能帮助推荐系统产生多样化、可解释的推荐结果,并更好地完成跨领域推荐、序列化推荐等具有挑战性的推荐任务,从而提升各类实际推荐场景中的用户满意度。本文将现有融入知识图谱的各种推荐模型按其采用的推荐算法与面向的推荐场景不同进行分类,构建科学、合理的分类体系。其中,按照推荐方法的不同,划分出基于特征表示的和基于图结构的两大类推荐模型;按推荐场景划分,特别关注多样化推荐、可解释推荐、序列化推荐与跨领域推荐。然后,我们在各类推荐模型中分别选取代表性的研究工作进行介绍,还简要对比了各个模型的特点与优劣。此外,本文还结合当下人工智能技术和应用的发展趋势,展望了认知智能推荐系统的发展前景,具体包括融合多模态知识的推荐系统,具有常识理解能力的推荐系统,以及解说式、劝说式、抗辩式推荐系统。本文的综述内容和展望可作为推荐系统未来研究方向的有益参考。     

基于本体的Web挖掘在个性化E-learning系统中的应用

针对当前e-learning系统中存在着资源利用效率低和学习内容单一、个性化不足等问题,设计一个基于本体的web挖掘的个性化e-learning系统,通过应用web挖掘和本体技术,使得该系统能根据学习者的知识结构、学习目标、学习风格、偏好等特征信息提供适应学习者的教学方法和学习资源,营造个性化的网络学习环境。实验表明本体技术能明显改善挖掘效果,提高学习资源库的管理效率,有效促进学生的网络学习,满足学生个性化学习的需求,为系统的决策分析提供了智能的辅助手段。     

A language learning support system using course-centered ontology and its evaluation

This paper presents a course-centered ontology for assisting learning support systems to embody the relations among knowledge points and also among the learning materials for those knowledge points. An “individual-class-individual” ontology design (first an individual-class design, then an innovative design about relations among bottom individuals), was applied to the construction of a course-centered ontology for an existing Japanese grammar course. Furthermore, a customizable language learning support system was built to manipulate the course-centered ontology to provide an interface for the learning objects arrangement which displays the visual representation of knowledge points and their relations. The intention underlying the development of the system is to encourage instructors to orient their teaching materials to specific knowledge points and even directly to relations between knowledge points. With these orientations, the learning support system is able to provide an environment in which learners can readily distinguish between related knowledge points. Finally, based on the result of a preliminary evaluation, a study to explore the impact of learning styles and learning habits on learning performance was conducted to further evaluate our ontology-based learning support system. The results of the study suggest three main points: (a) the experimental students who learned with our system achieved significantly better learning achievement than those who just did self-study with textbooks after studying the same target contents for 60 mins; (b) the learning achievement of experimental group was not related to either their learning style in Sequential/Global dimension or their habit of “learning from comparison”; (c) in terms of the learning perception of experimental group, compared to “Sequential learners”, most “Global learners” had a stronger feeling that the comparison function is useful in improving their learning performance, and the learners who don't habitually “learning from comparison” were more likely to suffer from lack of the attention and feel more pressure than those who do habitually “learning from comparison”.     

基于知识的电子商务智能推荐系统平台设计

分析了传统推荐技术存在的不足,阐述了基于知识的推荐技术的特点及其发展。针对现有基于知识的电子商务推荐系统中存在的不足,提出了基于知识的电子商务智能推荐需要解决的基本问题,设计了基于知识的电子商务智能推荐平台的逻辑框架,并阐述了其工作原理。     

A Multidimensional Paper Recommender: Experiments and Evaluations

Paper recommender systems in the e-learning domain must consider pedagogical factors, such as a paper's overall popularity and learner background knowledge — factors that are less important in commercial book or movie recommender systems. This article reports evaluations of a 6D paper recommender. Experimental results from a human subject study of learner preferences suggest that pedagogical factors help to overcome a serious cold-start problem (not having enough papers or learners to start the recommender system) and help the system more appropriately support users as they learn.     

Applications of wavelet data reduction in a recommender system

Memory-based collaborative filtering (CF) recommender systems have emerged as an effective technique for information filtering. CF recommenders are being widely adopted for e-commerce applications to assist users in finding and selecting items of interest. As a result, the scalability of CF recommenders presents a significant challenge; one that is particularly resilient because the volume of data these systems utilize will continue to increase over time. This paper examines the impact of discrete wavelet transformation (DWT) as an approach to enhance the scalability of memory-based collaborative filtering recommender systems. In particular, a wavelet transformation methodology is proposed and applied to both synthetic and real-world recommender ratings. For experimental purposes, the DWT methodology’s effect on predictive accuracy and calculation speed is evaluated to compare recommendation quality and performance.     

Hybrid Recommender Systems: Survey and Experiments

Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.     

融合学习者时序行为和认知水平的个性化学习资源推荐算法

个性化服务是构建智慧学习环境的内在要求和建设要点.为学习环境中的主体（学习者）推送个性化学习资源可以提高学习资源的利用概率,解决在线学习容易产生的迷航问题.通过本体知识库的统一性语义建立学习者和学习资源内部结构特征,设计出一个有效计算两者相关性的推荐算法.算法中引入时间衰减函数来描述学习者学习时的时序特征,导入计算学习者认知水平与学习资源难度的匹配算子以体现学习的循序渐进原则.实验结果表明：所构建的时间函数和匹配算子达到了预期目标,更好地提升了所推荐学习资源的质量和适应性,且算法的时间复杂度能满足实时计算要求.     

A hybrid E-learning recommendation integrating adaptive profiling and sentiment analysis

This research proposes a novel framework named Enhanced e-Learning Hybrid Recommender System (ELHRS) that provides an appropriate e-content with the highest predicted ratings corresponding to the learner’s particular needs. To accomplish this, a new model is developed to deduce the Semantic Learner Profile automatically. It adaptively associates the learning patterns and rules depending on the learner’s behavior and the semantic relations computed in the semantic matrix that mutually links e-learning materials and terms. Here, a semantic-based approach for term expansion is introduced using DBpedia and WordNet ontologies. Further, various sentiment analysis models are proposed and incorporated as a part of the recommender system to predict ratings of e-learning resources from posted text reviews utilizing fine-grained sentiment classification on five discrete classes. Qualitative Natural Language Processing (NLP) methods with tailored-made Convolutional Neural Network (CNN) are developed and evaluated on our customized dataset collected for a specific domain and a public dataset. Two improved language models are introduced depending on Skip-Gram (S-G) and Continuous Bag of Words (CBOW) techniques. In addition, a robust language model based on hybridization of these couple of methods is developed to derive better vocabulary representation, yielding better accuracy 89.1% for the CNN-Three-Channel-Concatenation model. The suggested recommendation methodology depends on the learner’s preferences, other similar learners’ experience and background, deriving their opinions from the reviews towards the best learning resources. This assists the learners in finding the desired e-content at the proper time.     

Improving neighborhood based Collaborative Filtering via integrated folksonomy information

Personalized recommender systems which can provide people with suggestions according to individual interests usually rely on Collaborative Filtering (CF). The neighborhood based model (NBM) is a common choice when implementing such recommenders due to the intuitive nature; however, the recommendation accuracy is a major concern. Current NBM based recommenders mostly address the accuracy issue based on the rating data alone, whereas research on hybrid recommender systems suggests that users enjoy specifying feedback about items across multiple dimensions. In this work we aim to improve the accuracy of NBM via integrating the folksonomy information. To achieve this objective, we first propose the folksonomy network (FN) to analyze the item relevance described by the folksonomy data. We subsequently integrate the obtained folksonomy information into the global-optimization based NBM for making multi-source based recommendations. Experiments on the MovieLens dataset suggest positive results, which prove the efficiency of our strategy.