Recommendation & mobile systems - a state of the art for tourism

Recommendation systems have been growing in number over the last fifteen years. To evolve and adapt to the demands of the actual society, many paradigms emerged giving birth to even more paradigms and hybrid approaches. These approaches contain strengths and weaknesses that need to be evaluated according to the knowledge area in which the system is going to be implemented. Mobile devices have also been under an incredible growth rate in every business area, and there are already lots of mobile based systems to assist tourists. This explosive growth gave birth to different mobile applications, each having their own advantages and disadvantages. Since recommendation and mobile systems might as well be integrated, this work intends to present the current state of the art in tourism mobile and recommendation systems, as well as to state their advantages and disadvantages

Trust networks for recommender systems

Recommender systems use information about their user’s profiles and relationships to suggest items that might be of interest to them. Recommenders that incorporate a social trust network among their users have the potential to make more personalized recommendations compared to traditional systems, provided they succeed in utilizing the additional (dis)trust information to their advantage. Such trust-enhanced recommenders consist of two main components: recommendation technologies and trust metrics (techniques which aim to estimate the trust between two unknown users.) We introduce a new bilattice-based model that considers trust and distrust as two different but dependent components, and study the accompanying trust metrics. Two of their key building blocks are trust propagation and aggregation. If user a wants to form an opinion about an unknown user x, a can contact one of his acquaintances, who can contact another one, etc., until a user is reached who is connected with x (propagation). Since a will often contact several persons, one also needs a mechanism to combine the trust scores that result from several propagation paths (aggregation). We introduce new fuzzy logic propagation operators and focus on the potential of OWA strategies and the effect of knowledge defects. Our experiments demonstrate that propagators that actively incorporate distrust are more accurate than standard approaches, and that new aggregators result in better predictions than purely bilattice-based operators. In the second part of the dissertation, we focus on the application of trust networks in recommender systems. After the introduction of a new detection measure for controversial items, we show that trust-based approaches are more effective than baselines. We also propose a new algorithm that achieves an immediate high coverage while the accuracy remains adequate. Furthermore, we also provide the first experimental study on the potential of distrust in a memory-based collaborative filtering recommendation process. Finally, we also study the user cold start problem; we propose to identify key figures in the network, and to suggest them as possible connection points for newcomers. Our experiments show that it is much more beneficial for a new user to connect to an identified key figure instead of making random connections

Web 2.0 technologies for learning: the current landscape – opportunities, challenges and tensions

This is the first report from research commissioned by Becta into Web 2.0 technologies for learning at Key Stages 3 and 4. This report describes findings from an additional literature review of the then current landscape concerning learner use of Web 2.0 technologies and the implications for teachers, schools, local authorities and policy makers

Innovative Firm Performance Management Using a Recommendation System Based on Fuzzy Association Rules: The Case of Vietnam’s Apparel Small and Medium Enterprises

Purpose: This study aims to apply a classification algorithm based-on fuzzy association rules (FARs) to improve the effectiveness of firms' performance prediction problem. Particularly, this study investigates potential FARs exists between inputs and outputs of firms' performance management process. These extracted FARs could be used to help firm’s managers make better dicision to improve firm’s performance.   Theoretical framework: Private enterprise development has been identified as key to Vietnam's economy that was commonly depended on state enterprise. For that, understanding and improving firms' performance and productivity is one of the most important tasks, from both macro and micro perspectives. There have been many studies on Vietnam's firm performance, but mostly relying on econometric methods that limit the understanding with structural equations. This study, instead, attempts to utilize new achievements of Artificial Intelligence (AI) for this task. Among AI techniques, fuzzy association rule is able to address the relationship between input factors and firm performance indicators. For each company, the finding FARs can be used to predict its performance and then change the business plan or react to improve weekness of organization.   Design/Methodology/Approach: The proposal model is applied on data of small and medium-sized enterprises (SMEs) of the apparel industry in Vietnam in the period 2010-2015. The sample consist of a total of 23637 observation of  Vietnam firms in apparel and textile industry and contains 16 main criterias for those firms.   Finding: A recommendation system (RS) is constructed from disclosed FARs and is a key factor in a novel innovative firms' performance management process. The percentage of classified instances using the mining FARs is not quite high (about 82%), but it is not always the case. Vietnam’s apparel dataset includes rare classes of ROA, therefore applying only frequent FARs is not enough. This issue can be fixed by using both frequent and infrequent FARs.       Research, practical & social implications: The proposed model has a great opportunity to use not only in the small and medium-sized enterprises (SMEs) of the apparel industry but other industrial sectors. FARs support the well-understand of firm performance to firm’s manager and help them better to react. Besides, FARs could be used to create RSs that makes alerts about risk automatically.   Originality/Value: The fact, our current study is the first to inspect the ability of FARs on SMEs of the apparel industry in Vietnam. This study provides theoritical potential knowledge and empirical evidence in the application of FARs technology in innovative firm’s management

A Review of Text Corpus-Based Tourism Big Data Mining

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years

A Review of Text Corpus-Based Tourism Big Data Mining

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years

Exploiting the conceptual space in hybrid recommender systems: a semantic-based approach

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 200