物件導向軟體之架構(Object-Oriented Software Construction)探討

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

Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware recommender systems.

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Context-Aware Recommender Systems

The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into account any additional contextual information, such as time, location, or the company of other people (e.g., for watching movies or dining out). In this chapter we argue that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations. We discuss the general notion of context and how it can be modeled in recommender systems. Furthermore, we introduce three different algorithmic paradigms – contextual prefiltering, post-filtering, and modeling – for incorporating contextual information into the recommendation process, discuss the possibilities of combining several contextaware recommendation techniques into a single unifying approach, and provide a case study of one such combined approach. Finally, we present additional capabilities for context-aware recommenders and discuss important and promising directions for future research.

/pdf/context-aware-recommender-systems-8uash1fwm4.pdf

Context-aware recommender systems

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

This paper presents INTRIGUE, a prototype tourist-information server that presents information about the area around Turin City, Italy, on desktop and hand held devices.This system recommends sightseeing destinations and itineraries by taking into account the preferences of heterogeneous tourist groups (such as families with children or the elderly) and explains the recommendations by addressing the group members' requirements. Moreover, the system provides an interactive agenda for scheduling the tour. The services offered by INTRIGUE rely on user modeling and adaptive hypermedia techniques; furthermore, XML-based technologies support the generation of the user interface and its adaptation to Web browsers and WAP minibrowsers.

/pdf/intrigue-personalized-recommendation-of-tourist-attractions-1dmpgabhmb.pdf

Intrigue: Personalized recommendation of tourist attractions for desktop and hand held devices

• Product features specify different types of information such as general data (price), aesthetic information (size, color), and technical information (functionalities, requirements). This characterization is exploited to customize the product presentations, according to the user’s interests. • More or less technically detailed descriptions are generated to suit the user’s knowledge. • The amount of information presented is tailored to the user’s interests and receptivity, showing only the most relevant data and hiding other details that the user can reach on demand as supplementary information.

Personalization in business-to-customer interaction

This paper describes the recommendation techniques exploited in INTRIGUE (INteractive TouRist Information GUidE), an adaptive recommender system that supports the organization of guided tours. This system recommends the places to visit by taking into account the characteristics of the group of participants and addressing the possibly conflicting preferences within the group. A group model is exploited to separately manage the preferences of heterogeneous subgroups of people and combine them, in order to identify solutions satisfactory for the group as a whole.

Tailoring the Recommendation of Tourist Information to Heterogeneous User Groups

Fault management in Web services composed by individual services from multiple suppliers currently relies on a local analysis that does not span across individual services, thus limiting the effectiveness of recovery strategies. We propose to address this limitation of current standards for Web service composition by employing model-based diagnosis to enhance fault analysis. We propose to add diagnostic Web services to the set of Web services providing the overall service, acting as supervisors of their execution, by identifying anomalies and explaining them in terms of faults to be repaired. This approach poses the basis for the development of specialized recovery and compensation techniques aimed at addressing different problems, which could not be otherwise discriminated.

/pdf/enhancing-web-services-with-diagnostic-capabilities-96wikwu7f4.pdf

Enhancing Web services with diagnostic capabilities

Although personalization and ubiquity are key properties for on-line services, they challenge the development of these systems due to the complexity of the required architectures In particular, the current infrastructures for the development of personalized, ubiquitous services are not flexible enough to accommodate the configuration requirements of the various application domains To address such issues, highly configurable infrastructures are neededIn this article, we describe Seta2000, an infrastructure for the development of recommender systems that support personalized interactions with their users and are accessible from different types of devices (eg, desktop computers and mobile phones) The Seta2000 infrastructure offers a built-in recommendation engine, based on a multi-agent architecture Moreover, the infrastructure supports the integration of heterogeneous software and the development of agents that can be configured to offer specialized facilities within a recommender system, but also to dynamically enable and disable such facilities, depending on the requirements of the application domain The Seta2000 infrastructure has been exploited to develop two prototypes: SeTA is an adaptive Web store personalizing the recommendation and presentation of products in the Web INTRIGUE is a personalized, ubiquitous information system suggesting attractions to possibly heterogeneous tourist groups

/pdf/a-multi-agent-infrastructure-for-developing-personalized-web-4zwl7hlgs2.pdf

Marino Segnan

Papers

Intrigue: Personalized recommendation of tourist attractions for desktop and hand held devices

Personalization in business-to-customer interaction

Tailoring the Recommendation of Tourist Information to Heterogeneous User Groups

Enhancing Web services with diagnostic capabilities

A multi-agent infrastructure for developing personalized web-based systems