A Process Framework for Semantics-aware Tourism Information Systems

The growing sophistication of user requirements in tourism due to the advent of new technologies such as the Semantic Web and mobile computing has imposed new possibilities for improved intelligence in Tourism Information Systems (TIS). Traditional software engineering and web engineering approaches cannot suffice, hence the need to find new product development approaches that would sufficiently enable the next generation of TIS. The next generation of TIS are expected among other things to: enable semantics-based information processing, exhibit natural language capabilities, facilitate inter-organization exchange of information in a seamless way, and evolve proactively in tandem with dynamic user requirements. In this paper, a product development approach called Product Line for Ontology-based Semantics-Aware Tourism Information Systems (PLOSATIS) which is a novel hybridization of software product line engineering, and Semantic Web engineering concepts is proposed. PLOSATIS is presented as potentially effective, predictable and amenable to software process improvement initiatives

A model for mobile content filtering on non-interactive recommendation systems

To overcome the problem of information overloading in mobile communication, a recommendation system can be used to help mobile device users. However, there are problems relating to sparsity of information from a first-time user in regard to initial rating of the content and the retrieval of relevant items. In order for the user to experience personalized content delivery via the mobile recommendation system, content filtering is necessary. This paper proposes an integrated method by using classification and association rule techniques for extracting knowledge from mobile content in a user's profile. The knowledge can be used to establish a model for new users and first rater on mobile content. The model recommends relevant content in the early stage during the connection based on the user's profile. The proposed method also facilitates association to be generated to link the first rater items to the top items identified from the outcomes of the classification and clustering processes. This can address the problem of sparsity in initial rating and new user's connection for non-interactive recommendation systems

Collaborative filtering recommendation system : a framework in massive open online courses

Massive open online courses (MOOCs) are growing relatively rapidly in the education environment. There is a need for MOOCs to move away from its one-size-fit-all mode. This framework will introduce an algorithm based recommendation system, which will use a collaborative filtering method (CFM). Collaborative filtering method (CFM) is the process of evaluating several items through the rating choices of the participants. Recommendation system is widely becoming popular in online study activities; we want to investigate its support to learning and encouragement to more effective participation. This research will be reviewing existing literature on recommender systems for online learning and its support to learners’ experiences. Our proposed recommendation system will be based on course components rating. The idea was for learners to rate the course and components they have studied in the platform between the scales of 1 – 5. After the rating, we then extract the values into a comma separated values (CSV) file then implement recommendation using Python programming based on learners with similar rating patterns. The aim was to recommend courses to different users in a text editor mode using Python programming. Collaborative filtering will act upon similar rating patterns to recommend courses to different learners, so as to enhance their learning experience and enthusiasm

Automating Software Customization via Crowdsourcing using Association Rule Mining and Markov Decision Processes

As systems grow in size and complexity so do their configuration possibilities. Users of modern systems are easy to be confused and overwhelmed by the amount of choices they need to make in order to fit their systems to their exact needs. In this thesis, we propose a technique to select what information to elicit from the user so that the system can recommend the maximum number of personalized configuration items. Our method is based on constructing configuration elicitation dialogs through utilizing crowd wisdom. A set of configuration preferences in form of association rules is first mined from a crowd configuration data set. Possible configuration elicitation dialogs are then modeled through a Markov Decision Processes (MDPs). Within the model, association rules are used to automatically infer configuration decisions based on knowledge already elicited earlier in the dialog. This way, an MDP solver can search for elicitation strategies which maximize the expected amount of automated decisions, reducing thereby elicitation effort and increasing user confidence of the result. We conclude by reporting results of a case study in which this method is applied to the privacy configuration of Facebook

Design of a recommender system for web based learning

The design of recommender systems is an ongoing research area where several researchers have devised means of incorporating intelligence in web content systems to be able to provide recommendations to learners on the basis of their learning preferences i.e. based on their learning profiles. The paper discusses the design of such a system based mapped to a content ontology and learner profiles created in the system

A Survey of Data Mining Techniques for Smart Museum Applications

This research aims to find out what data mining techniques are effectively implemented in museums and what application trends are currently being used to improve museum performance towards modern museums based on intelligent system technology. The review was carried out on a number of articles found in journals and proceedings in the 2004-2020 period. It is found that the majority of data mining techniques are implemented in museum virtual guide applications, recommender systems, collection clustering and classification system, and   visitor behaviour prediction application. Data classification, clustering, and prediction technique commonly used for museum application.  Collections with historical and artistic value  contain a lot of knowledge making data mining an important technique to be included in various applications in museums so that they can have an impact on the achievement of museum goals not only in the fields of education and culture but also economics and business

A Software Product Line Approach to Ontology-based Recommendations in E-Tourism Systems

This study tackles two concerns of developers of Tourism Information Systems (TIS). First is the need for more dependable recommendation services due to the intangible nature of the tourism product where it is impossible for customers to physically evaluate the services on offer prior to practical experience. Second is the need to manage dynamic user requirements in tourism due to the advent of new technologies such as the semantic web and mobile computing such that etourism systems (TIS) can evolve proactively with emerging user needs at minimal time and development cost without performance tradeoffs. However, TIS have very predictable characteristics and are functionally identical in most cases with minimal variations which make them attractive for software product line development. The Software Product Line Engineering (SPLE) paradigm enables the strategic and systematic reuse of common core assets in the development of a family of software products that share some degree of commonality in order to realise a significant improvement in the cost and time of development. Hence, this thesis introduces a novel and systematic approach, called Product Line for Ontology-based Tourism Recommendation (PLONTOREC), a special approach focusing on the creation of variants of TIS products within a product line. PLONTOREC tackles the aforementioned problems in an engineering-like way by hybridizing concepts from ontology engineering and software product line engineering. The approach is a systematic process model consisting of product line management, ontology engineering, domain engineering, and application engineering. The unique feature of PLONTOREC is that it allows common TIS product requirements to be defined, commonalities and differences of content in TIS product variants to be planned and limited in advance using a conceptual model, and variant TIS products to be created according to a construction specification. We demonstrated the novelty in this approach using a case study of product line development of e-tourism systems for three countries in the West-African Region of Africa