A Novel Data Mining And Knowledge Discovery Framework For Digital Library Recommendations System Based On User S Feedback And Personalization
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A Novel Data Mining and Knowledge Discovery Framework for Digital Library Recommendations System Based on User's Feedback and Personalization
The research field of digital libraries involves a combination of different subfields from a wide variety of domains, and the usability and popularity of digital libraries depend on the satisfaction and user experiences of people while engaging with digital library systems. The digital library collections are nothing but large-scale resource search and retrieval systems, that are increasingly playing an important role in our knowledge society. They can be defined as catalogued and curated collections of different types of information resources, that are stored in a digital format in digital places. They can be viewed as online resource hubs, where it is possible to access information anytime and anywhere. It is important that every digital library needs to be equipped with a wide set of tools, assisting the users in their resource search and retrieval activities, and enhance their user-experience with these activities, leading to better usability of the system, and help users in finding relevant information and getting tailored information based on the personalized feedback, feelings and sentiments, ratings, preferences, and metadata build within the system. One of the tools that facilitates such a wide set of tailored and personalisation option, is called the recommender system, synonymously called the recommendation system, as well. The digital library systems equipped with recommendation system tools aim to provide personalisation features and assist users in finding relevant information based on their preferences, based on their previous choices, maintained in their respective profiles, and other similar users with either similar profiles or usage behaviour and pattern. The success of the recommendation process and tailored retrieval performance of the recommender system depends on the quality and quantity of the information used about the items and users, and their profiles stored from each interaction since the profiles represent the information needs of the users from the user-item interactions. To identify the needs of an individual user, and provide better personalisation, the design of the recommender system needs to be based on models that maintain detailed item descriptions and accurate users' profiles, their preferences, and details of item-user interactions, and the performance and robustness of the entire recommendation system rely on the modelling accuracy on these factors. Due to massive information available about items and resources, often users are faced with ambiguity and diversity of information, due to the enormous growth of the Internet, with increasingly complex and massive volumes of the information, leading to difficulties in isolating the content that fits their needs. Due to this, users are not always certain of their information needs, nor do they know exactly how to describe what they want. The most challenging task involved in building tailored and personalized recommendations is acquiring information on user needs and their preferences, when there is limited information about users, particularly when the users are new, or do not engage with the system often. These problems make it difficult to profile users accurately and provide quality recommendations. Traditional approaches to address this issue in the research literature has been based on the concept of building taxonomical representation of the item and model the interaction information about each user and the item, and the relevance of each item to users and several other users of similar type. Such item representations or a taxonomical model can assist in determining users' preferences. These models involve a hierarchical structure, comprising the coarse-grained representation to the fine-grained representations, for symbolizing the set of categories or topics, items, and users. However, due to the complex relationship between concepts, items, and users, from massive data sources, these taxonomic representation models are limited in their capabilities, and identification of each user's information need is still a challenging task, particularly, when the user interaction with the items is limited, or the user happens to be new to the system. This thesis attempts to address some of these challenges, and proposes a novel computational framework based on deep machine learning techniques for building recommendation systems for digital libraries (modelled similar to a large-scale resource search and retrieval system), by including user feedbacks, preferences, explicit ratings, sentiments, and metadata information. In particular, the two deep learning models proposed in this thesis, the Multilayer Convolutional Neural Networks (MCNN) model and Collaborative Filtering based Multistage Deep Neural Network architecture (CFMDNN) model, allow better interaction and collaboration events to be captured between users' feedbacks, explicit ratings, sentiments, and metadata information. The evaluation of the proposed computational framework based on several publicly available datasets shows significant enhancement in the recommendation systems performance, outperforming the related baselines. Further, the extension of these novel deep learning models for a new multilingual context (Arabic /English) digital library systems context, has led to promising results. In summary, this thesis makes significant contributions to recommender systems for digital library systems applications, with models based on multiple components including, explicit ratings, sentiment analysis based on text feedback, and metadata information for both Arabic and English languages contexts.
HCI International 2023 – Late Breaking Papers
This seven-volume set LNCS 14054-14060 constitutes the proceedings of the 25th International Conference, HCI International 2023, in Copenhagen, Denmark, in July 2023. For the HCCII 2023 proceedings, a total of 1578 papers and 396 posters was carefully reviewed and selected from 7472 submissions. Additionally, 267 papers and 133 posters are included in the volumes of the proceedings published after the conference, as “Late Breaking Work”. These papers were organized in the following topical sections: HCI Design and User Experience; Cognitive Engineering and Augmented Cognition; Cultural Issues in Design; Technologies for the Aging Population; Accessibility and Design for All; Designing for Health and Wellbeing; Information Design, Visualization, Decision-making and Collaboration; Social Media, Creative Industries and Cultural Digital Experiences; Digital Human Modeling, Ergonomics and Safety; HCI in Automated Vehicles and Intelligent Transportation; Sustainable GreenSmart Cities and Smart Industry; eXtended Reality Interactions; Gaming and Gamification Experiences; Interacting with Artificial Intelligence; Security, Privacy, Trust and Ethics; Learning Technologies and Learning Experiences; eCommerce, Digital Marketing and eFinance.
Recommender Systems
This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.