IMPROVING THE DEPENDABILITY OF DESTINATION RECOMMENDATIONS USING INFORMATION ON SOCIAL ASPECTS

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Recommendation technique-based government-to-business personalized e-services

One of the new directions in current e-government development is to provide personalized online services to citizens and businesses. Recommendation techniques can bring a possible solution for this issue. This study proposes a hybrid recommendation approach to provide personalized government to business (G2B) e-services. The approach integrates fuzzy sets-based semantic similarity and traditional item-based collaborative filtering methods to improve recommendation accuracy. A recommender system named Intelligent Business Partner Locator (IBPL) is designed to apply the proposed recommendation approach for supporting government agencies to recommend business partners. ©2009 IEEE

Improving the Dependability of Destination Recommendations using Information on Social Aspects

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process.Content-based filtering; Recommender Systems; Ontology; Social Attributes, Destination recommendation

What’s going on in my city? Recommender systems and electronic participatory budgeting

In this paper, we present electronic participatory budgeting (ePB) as a novel application domain for recommender systems. On public data from the ePB platforms of three major US cities – Cambridge, Miami and New York City–, we evaluate various methods that exploit heterogeneous sources and models of user preferences to provide personalized recommendations of citizen proposals. We show that depending on characteristics of the cities and their participatory processes, particular methods are more effective than others for each city. This result, together with open issues identified in the paper, call for further research in the area

Personalized government online services with recommendation techniques

University of Technology, Sydney. Faculty of Information Technology.With the integration of information from different government agencies, a vast resource of information and services may be gathered in one portal. Many businesses have difficulty locating the required information and services. In such a situation of vast information overload, one of the difficulties facing governments is how to provide businesses with information specific to their needs, rather than an undifferentiated mass of information. One way to do this is through the development of personalized government online services. Indeed, the recent Accenture e-government study indicates that personalization techniques in e-government are beginning to emerge. However, existing personalization with recommendation techniques focuses on text document retrieval and e-commerce product recommendation domain. Personalization and recommendation applications in e-government have paid relatively little research attention. Many mechanisms have been developed to deliver only relevant information to web users and prevent information overload. The most popular recent developments in the e- commerce domain are the user-preference based personalization and recommendation techniques. The existing techniques have a major drawback: they are difficulty to generate recommendation on one-and-only items, because most of them do not understand the item’s semantic features and attributes. Therefore, this study aims to: (1) propose a novel approach, semantic product relevance model and its attendant personalized recommendation technique, to handle the one-and-only item recommendation problem; (2) develop a recommender system prototype, called Smart Trade Exhibition Finder, to tailor the relevant trade exhibition information to each particular business user, and to assist export business selecting the right trade exhibitions for market promotion. Smart Trade Exhibition Finder may reduce significantly the time, cost and risk faced by exporters in selecting, entering and developing international markets. In particular, the proposed approach can be used to overcome the drawback of existing recommendation techniques and enable recommender systems to work within a much wider range of problems which cannot currently be handled. The outcome of this study will solve the rating data lacking and new item problem, and significantly improve the performance compared to existing recommendation techniques

Hybrid recommender systems for personalized government-to-business e-services

University of Technology, Sydney. Faculty of Engineering and Information Technology.As e-Governments around the world face growing pressures to improve the quality of service delivery and become more efficient and cost-effective, their initiatives currently focus on providing users with a seamless service delivery experience. Webbased technologies offer governments more efficient and effective means than traditional physical channels to provide high quality e-Service delivery to their users, which include citizens and businesses. Government-to-Business (G2B) e-Services involve information distribution, transactions, and interactions with businesses m varying ways via e-Government websites and portals. The G2B e-Services aim to reduce burdens on businesses and to provide effective and efficient access to information for business users. One of the most important e-Services of G2B is the promotion of local businesses goods and services to consumers (i.e., local and overseas businesses) by providing on line business directories. However, with the rapid growth of information and unreliable search facilities, busine s users, who are seeking 'one-to-one' e-Services from government in highly competitive markets, struggle with online business directories and increasingly find it difficult to locate business pa1tners according to their needs and interests. How, then, can business users be provided with inforn1ation and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this research is the development of personalized G2B e-Services using recommender systems. It is worth mentioning that the adoption of recommender systems in the context of e- Government to provide personalized services has received very limited attention in the literature. Recommender systems aim to suggest the right items (products, services or information) that best match the needs and interests of particular users based on their explicit and implicit preferences. In current recommender systems, the Collaborative Filtering (CF) approaches are the most popular and widely adopted recommendation approaches. Regardless of the success of CF-based approaches in various recommendation applications, they still suffer from data uncertainty, data sparsity, cold-start item and cold-start user problems, resulting in poor recommendation accuracy and reduced coverage. An effective solution proposed in this research to alleviate such problems is the development of hybrid and fusion-based recommendation algorithms that exploit and incorporate additional knowledge about users and items. Such knowledge can be extracted from either the users ' trust social network or the items' semantic domain knowledge. This research explores the adoption of recommender systems m an e- Govemment context for the provision of personalized G2B e-Services. Accordingly, a G2B recommendation framework for providing personalized G2B e-Services (particularly personalized business partner recommendations) for Small-to-Medium Businesses (SMBs) is proposed. Novel hybrid and fusion-based recommendation models and algorithms are also proposed and developed to overcome the limitations of existing CF-based recommendation approaches. Experimental results on real datasets show that our proposed recommendation algorithms significantly outperfmm existing recommendation algorithms in terms of recommendation accuracy and coverage when dealing with data sparsity, cold-start item and cold-start user limitations inherent in CF-based recommendation approaches

A framework for delivering personalized e-Government tourism services

E-government (e-Gov) has become one of the most important parts of government strategies. Significant efforts have been devoted to e-Gov tourism services in many countries because tourism is one of the major profitable industries. However, the current e-Gov tourism services are limited to simple online presentation of tourism information. Intelligent e-Gov tourism services, such as the personalized e-Gov (Pe-Gov) tourism services, are highly desirable for helping users decide "where to go, and what to do/see" amongst massive number of destinations and enormous attractiveness and activities. This paper proposes a framework of Pe-Gov tourism services using recommender system techniques and semantic ontology. This framework has the potential to enable tourism information seekers to locate the most interesting destinations with the most suitable activities with the least search efforts. Its workflow and some outstanding features are depicted with an example