An Introduction to MultiAgent Systems.

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education, and other domains, with more recent successful developments of recommender systems for real-world applications becoming apparent. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but more importantly of the practical developments in recommender systems. This paper therefore reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories: e-government, e-business, e-commerce/e-shopping, e-library, e-learning, e-tourism, e-resource services and e-group activities, and summarizes the related recommendation techniques used in each category. It systematically examines the reported recommender systems through four dimensions: recommendation methods (such as CF), recommender systems software (such as BizSeeker), real-world application domains (such as e-business) and application platforms (such as mobile-based platforms). Some significant new topics are identified and listed as new directions. By providing a state-of-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications. Research papers on various recommender system applications are summarized.The recommender systems are examined systematically through four dimensions.The recommender system applications are classified into eight categories.Related recommendation techniques in each category are identified.Several new recommendation techniques and application areas are uncovered.

Recommender system application developments

Recommender systems are currently being applied in many different domains. This paper focuses on their application in tourism. A comprehensive and thorough search of the smart e-Tourism recommenders reported in the Artificial Intelligence journals and conferences since 2008 has been made. The paper provides a detailed and up-to-date survey of the field, considering the different kinds of interfaces, the diversity of recommendation algorithms, the functionalities offered by these systems and their use of Artificial Intelligence techniques. The survey also provides some guidelines for the construction of tourism recommenders and outlines the most promising areas of work in the field for the next years.

Review: Intelligent tourism recommender systems: A survey

Consumers acceptance of information and communications technology in tourism

Research highlights? Recommender system able to provide recommendations for groups of tourists. ? Group members must be equally satisfied with the recommendation. ? Two techniques are explored: intersection and aggregation. ? Intersection obtains better results, it brings together the preferences of all group members. This paper presents a recommender system for tourism based on the tastes of the users, their demographic classification and the places they have visited in former trips. The system is able to offer recommendations for a single user or a group of users. The group recommendation is elicited out of the individual personal recommendations through the application of mechanisms such as aggregation and intersection. The elicitation mechanism is implemented as an extension of e-Tourism, a user-adapted tourism and leisure application whose main component is the Generalist Recommender System Kernel (GRSK), a domain-independent taxonomy-driven recommender system.

On the design of individual and group recommender systems for tourism

e-Tourism is a tourist recommendation and planning application to assist users on the organization of a leisure and tourist agenda. First, a recommender system offers the user a list of the city places that are likely of interest to the user. This list takes into account the user demographic classification, the user likes in former trips and the preferences for the current visit. Second, a planning module schedules the list of recommended places according to their temporal characteristics as well as the user restrictions; that is the planning system determines how and when to realize the recommended activities. Having the list of recommended activities organized as an agenda (i.e. an executable plan), is a relevant characteristic that most recommender systems lack.

/pdf/e-tourism-a-tourist-recommendation-and-planning-application-13iunvsit1.pdf

e-TOURISM: A TOURIST RECOMMENDATION AND PLANNING APPLICATION

Preference elicitation techniques for group recommender systems

e-Tourism is a tourist recommendation and planning application to assist users on the organization of a leisure and tourist agenda. First, a recommender system offers the user a list of the city places that are likely of interest to the user. This list takes into account the user demographic classification, the user likes in former trips and the preferences for the current visit. Second, a planning module schedules the list of recommended places according to their temporal characteristics as well as the user restrictions; that is the planning system determines how and when to perform the recommended activities. This is a very relevant feature that most recommender systems lack as it allows the user to have the list of recommended activities organized as an agenda, i.e. to have a totally executable plan.

/pdf/e-tourism-a-tourist-recommendation-and-planning-application-2oq73j4iwa.pdf

e-Tourism: A Tourist Recommendation and Planning Application

This paper introduces a method for giving recommendations of tourist activities to a group of users. This method makes recommendations based on the group tastes, their demographic classification and the places visited by the users in former trips. The group recommendation is computed from individual personal recommendations through the use of techniques such as aggregation, intersection or incremental intersection. This method is implemented as an extension of the e-Tourism tool, which is a user-adapted tourism and leisure application, whose main component is the Generalist Recommender System Kernel (GRSK) , a domain-independent taxonomy-driven search engine that manages the group recommendation.

Inma Garcia

Papers

On the design of individual and group recommender systems for tourism

e-TOURISM: A TOURIST RECOMMENDATION AND PLANNING APPLICATION

Preference elicitation techniques for group recommender systems

e-Tourism: A Tourist Recommendation and Planning Application

A Group Recommender System for Tourist Activities