Semantic-enhanced hybrid recommender systems for personalised e-Government services

University of Technology, Sydney. Faculty of Engineering and Information Technology.E-Government is becoming ever more active in terms of improving the provision of services to citizens from a citizen-centred perspective, in which online services and information are delivered to citizens on a personalised basis. Some developed governments have started to offer personalised services through their official portals. However, the personalised services that are offered are mostly limited to static customisation and are therefore far from achieving effective citizen-centred e-Government services. Furthermore, delivering personalised online services that match the different needs and interests of government users is a challenge for e-Government, specifically in connection with the increasing information and services that are offered through the medium of government portals. Therefore, more advanced and intelligent e-Government systems are desirable. Personalisation techniques, particularly in the form of recommender systems, are promising to provide better solutions to support the development of personalisation in e-Government services. Furthermore, semantic enhanced recommender systems can better support citizen-centred e-Government services and enhance recommendation accuracy. The success of semantic enhanced hybrid recommendation approaches and the citizen-centric initiative of e-Government have fostered the idea of developing personalised e-Government recommendation service systems using semantic enhanced hybrid recommender systems. Accordingly, the effectiveness of utilising the semantic knowledge of e-Government services to enhance the recommendation quality of offered services is addressed in this thesis. This thesis makes five significant contributions to the area of e-Government personalised recommendation services. These contributions are summarised as follows: (i) the thesis first proposes a general framework for offering personalised e-Government services from a citizen-centred perspective based on the available user profiles information and semantic knowledge of a specific e-Government domain of interest; (ii) based on this general framework, a personalised e-Government tourism service recommendation framework is also proposed and considered as a target domain in this research study; (iii) new semantic enhanced hybrid recommendation approaches are developed to support the implementation of the recommendation generator engines of the proposed e-Government frameworks. The recommendation generator engines represent the core components of the proposed frameworks; (iv) new semantic similarity measures based on semantic knowledge of a target domain ontology are proposed to effectively evaluate the similarity between e-Government service items. The new semantic similarity measures are incorporated within the proposed hybrid approaches to improve the quality and accuracy of recommendations and to overcome the limitations of existing hybrid recommendation approaches; and (v) a switching semantic enhanced hybrid recommendation system is further proposed to enhance the overall quality of recommendation, address the sparsity, the cold-start user and item problems. Experimental evaluations of the proposed semantic enhanced hybrid recommendation approaches and switching system, on a real world tourism dataset, show promising results against state-of-the-art recommendation approaches in terms of the quality of recommendations, capacity to alleviate the sparsity, cold-start item and user problems

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Hybrid group recommendations for a travel service

Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations