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

THE INFLUENCE OF SOCIAL PRESENCE ON EVALUATING PERSONALIZED RECOMMENDER SYSTEMS

Providing recommendations is acknowledged as an important feature of a business-to-consumer online storefront. Although many studies have been conducted the algorithms and operational procedures relating to personalized recommender systems, empirical evidence demonstrating relationships between social presence and two important outcomes of evaluating recommender systems, reuse intention and trust, remains lacking. To test the existence of a causal link between social presence and reuse intention, and the mediating role of trust between these two variables, this study conducted experiments varying the levels of social presence while providing personalized recommendations to users based on their explicit preferences. This study also compared these effects in two different product contexts: hedonic and utilitarian products. Interactions of social presence and customer reviews were also investigated in these experiments. The results show that higher social presence increases both reuse intention and trust in recommender systems. In addition, the influence of social presence on reuse intention in the context of recommending utilitarian products is less than that in the context of recommending hedonic products

Semantic Recommender System

Though Content-based recommender systems proved to have better quality than Collaborative Filtering recommenders, the later is more used because the former suffers from complex mathematical calculations and inadequate data modeling techniques. Using Ontology(ies) to model the data allows machines to better understand both items and users’ preferences and thus not just suggesting better recommendations, but also providing accurate justifications. In this work we present a Semantic Recommender system that uses a novel way of generating recommendations depending on a Recommender Ontology that provides controlled vocabularies in the context of recommendations, and that is built upon the idea that not all classes and properties are important from item-similarities point of view. If the domain Ontology is annotated with the Recommender Ontology, the Semantic Recommender should be able to generate recommendations. As a result, the proposed system works with any domain data. Thanks to The Semantic Web standards. The proposed mathematical model takes into consideration, in addition to items’ features and users’ profiles, the context of the users and the temporal context, so some items, as an event’s ticket, should never be recommended if the event is over, and should get more presence before the event. The Recommender Ontology grants business owners a way to boost the recommended items according to their needs. This guarantees more diversity, which satisfies the business requirements. For the experiments, we have tested the proposed solution with many domains including movies, books, music, and with a real business company. We got 55% accuracy when testing on a movie domain though we knew just one feature about the movies. The main limitation we have faced is the absent of a content-based domain case that contains ABox, TBox, and ratings together

Recommender systems in industrial contexts

This thesis consists of four parts: - An analysis of the core functions and the prerequisites for recommender systems in an industrial context: we identify four core functions for recommendation systems: Help do Decide, Help to Compare, Help to Explore, Help to Discover. The implementation of these functions has implications for the choices at the heart of algorithmic recommender systems. - A state of the art, which deals with the main techniques used in automated recommendation system: the two most commonly used algorithmic methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization methods are detailed. The state of the art presents also purely content-based methods, hybridization techniques, and the classical performance metrics used to evaluate the recommender systems. This state of the art then gives an overview of several systems, both from academia and industry (Amazon, Google ...). - An analysis of the performances and implications of a recommendation system developed during this thesis: this system, Reperio, is a hybrid recommender engine using KNN methods. We study the performance of the KNN methods, including the impact of similarity functions used. Then we study the performance of the KNN method in critical uses cases in cold start situation. - A methodology for analyzing the performance of recommender systems in industrial context: this methodology assesses the added value of algorithmic strategies and recommendation systems according to its core functions.Comment: version 3.30, May 201