Browsing by Author "Jafari, Ali"

Now showing 1 - 7 of 7
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    Community Recommendation in Social Networks with Sparse Data
    (2020-12) Rahmaniazad, Emad; King, Brian; Jafari, Ali; Salama, Paul
    Recommender systems are widely used in many domains. In this work, the importance of a recommender system in an online learning platform is discussed. After explaining the concept of adding an intelligent agent to online education systems, some features of the Course Networking (CN) website are demonstrated. Finally, the relation between CN, the intelligent agent (Rumi), and the recommender system is presented. Along with the argument of three different approaches for building a community recommendation system. The result shows that the Neighboring Collaborative Filtering (NCF) outperforms both the transfer learning method and the Continuous bag-of-words approach. The NCF algorithm has a general format with two various implementations that can be used for other recommendations, such as course, skill, major, and book recommendations.
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    Design and Development of an Intelligent Online Personal Assistant in Social Learning Management Systems
    (2019-05) Hosseini Asanjan, Seyed Mahmood; King, Brian; Ben Miled, Zina; Jafari, Ali
    Over the past decade, universities had a significant improvement in using online learning tools. A standard learning management system provides fundamental functionalities to satisfy the basic needs of its users. The new generation of learning management systems have introduced a novel system that provides social networking features. An unprecedented number of users use the social aspects of such platforms to create their profile, collaborate with other users, and find their desired career path. Nowadays there are many learning systems which provide learning materials, certificates, and course management systems. This allows us to utilize such information to help the students and the instructors in their academic life. The presented research work's primary goal is to focus on creating an intelligent personal assistant within the social learning systems. The proposed personal assistant has a human-like persona, learns about the users, and recommends useful and meaningful materials for them. The designed system offers a set of features for both institutions and members to achieve their goal within the learning system. It recommends jobs and friends for the users based on their profile. The proposed agent also prioritizes the messages and shows the most important message to the user. The developed software supports model-controller-view architecture and provides a set of RESTful APIs which allows the institutions to integrate the proposed intelligent agent with their learning system.
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    Developing a dynamic recommendation system for personalizing educational content within an E-learning network
    (2018) Mirzaeibonehkhater, Marzieh; King, Brian; Jafari, Ali; Liu, Hongbo
    This research proposed a dynamic recommendation system for a social learning environment entitled CourseNetworking (CN). The CN provides an opportunity for the users to satisfy their academic requirement in which they receive the most relevant and updated content. In our research, we extracted some implicit and explicit features from the system, which are the most relevant user feature and posts features. The selected features are used to make a rating scale between users and posts so that represent the link between user and post in this learning management system (LMS). We developed an algorithm which measures the link between each user and post for the individual. To achieve our goal in our system design, we applied natural language processing technique (NLP) for text analysis and applied various classi cation technique with the aim of feature selection. We believe that considering the content of the posts in learning environments as an impactful feature will greatly affect to the performance of our system. Our experimental results demonstrated that our recommender system predicts the most informative and relevant posts to the users. Our system design addressed the sparsity and cold-start problems, which are the two main challenging issues in recommender systems.
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    Heterogeneous Graph Based Neural Network for Social Recommendations with Balanced Random Walk Initialization
    (2020-12) Salamat, Amirreza; King, Brian; Jafari, Ali; Luo, Xiao
    Research on social networks and understanding the interactions of the users can be modeled as a task of graph mining, such as predicting nodes and edges in networks. Dealing with such unstructured data in large social networks has been a challenge for researchers in several years. Neural Networks have recently proven very successful in performing predictions on number of speech, image, and text data and have become the de facto method when dealing with such data in a large volume. Graph NeuralNetworks, however, have only recently become mature enough to be used in real large-scale graph prediction tasks, and require proper structure and data modeling to be viable and successful. In this research, we provide a new modeling of the social network which captures the attributes of the nodes from various dimensions. We also introduce the Neural Network architecture that is required for optimally utilizing the new data structure. Finally, in order to provide a hot-start for our model, we initialize the weights of the neural network using a pre-trained graph embedding method. We have also developed a new graph embedding algorithm. We will first explain how previous graph embedding methods are not optimal for all types of graphs, and then provide a solution on how to combat those limitations and come up with a new graph embedding method.
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    Managing Courses, Defining Learning: What Faculty, Students, and Administrators Want
    (EDUCAUSE, 2006-07-01) Jafari, Ali; McGee, Patricia; Carmean, Colleen
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    Proposing a New System Architecture for Next Generation Learning Environment
    (2016) Aboualizadehbehbahani, Maziar; King, Brian; Jafari, Ali; Wu, Huanmei
    The emergence of information exchange and act of offering features through external interfaces is a vast but immensely valuable challenge, and essential elements of learning environments cannot be excluded. Nowadays, there are a lot of different service providers working in the learning systems market and each of them has their own advantages. On that premise, in today's world even large learning management systems are trying to cooperate with each other in order to be best. For instance, Instructure is a substantial company and can easily employ a dedicated team tasked with the development of a video conferencing functionality, but it chooses to use an open source alternative instead: The BigBlueButton. Unfortunately, different learning system manufacturers are using different technologies for various reasons, making integration that much harder. Standards in learning environments have come to resolve problems regarding exchanging information, providing and consuming functionalities externally and simultaneously minimizing the amount of effort needed to integrate systems. In addition to defining and simplifying these standards, careful consideration is essential when designing new, comprehensive and useful systems, as well as adding interoperability to existing systems, all which subsequently took part in this research. In this research I have reviewed most of the standards and protocols for integration in learning environments and proposed a revised approach for app stores in learning environments. Finally, as a case study, a learning tool has been developed to avail essential functionalities of a social educational learning management system integrated with other learning management systems. This tool supports the dominant and most popular standards for interoperability and can be added to learning management systems within seconds.
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    Recommendation Systems in Social Networks
    (2023-05) Mohammad Jafari, Behafarid; King, Brian; Luo, Xiao; Jafari, Ali; Zhang, Qingxue
    The dramatic improvement in information and communication technology (ICT) has made an evolution in learning management systems (LMS). The rapid growth in LMSs has caused users to demand more advanced, automated, and intelligent services. CourseNet working is a next-generation LMS adopting machine learning to add personalization, gamifi cation, and more dynamics to the system. This work tries to come up with two recommender systems that can help improve CourseNetworking services. The first one is a social recommender system helping CourseNetworking to track user interests and give more relevant recommendations. Recently, graph neural network (GNN) techniques have been employed in social recommender systems due to their high success in graph representation learning, including social network graphs. Despite the rapid advances in recommender systems performance, dealing with the dynamic property of the social network data is one of the key challenges that is remained to be addressed. In this research, a novel method is presented that provides social recommendations by incorporating the dynamic property of social network data in a heterogeneous graph by supplementing the graph with time span nodes that are used to define users long-term and short-term preferences over time. The second service that is proposed to add to Rumi services is a hashtag recommendation system that can help users label their posts quickly resulting in improved searchability of content. In recent years, several hashtag recommendation methods are proposed and de veloped to speed up processing of the texts and quickly find out the critical phrases. The methods use different approaches and techniques to obtain critical information from a large amount of data. This work investigates the efficiency of unsupervised keyword extraction methods for hashtag recommendation and recommends the one with the best performance to use in a hashtag recommender system.