PersonalWeb: An extensible framework to recommend web and Personal Information (original) (raw)
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Proceedings of the 2nd Workshop on Context-awareness in Retrieval and Recommendation - CaRR '12, 2012
The aim of the second CaRR workshop was to invite the community to a discussion on new, creative ways to handle context-awareness. Furthermore, the workshop aimed at improving the exchange of ideas between different communities involved in research concerning human-computer interaction, machine learning, information retrieval and recommendation. The organizers would like to thank all the authors for contributing to CaRR 2012 and all the members of the program committee for ascertaining the scientific quality of the workshop. Additional information about the workshop is provided at the workshop website http://www.carr-workshop.org
Context-aware Recommender Systems J. UCS Special Issue
Journal of Universal Computer Science, 2010
Recommender systems have been researched and deployed extensively over the last decade in various application areas, including e-commerce, technology enhanced learning, e-health, adaptive multimedia and knowledge management. The three approaches of recommender systems commonly implemented are collaborative filtering, content-based filtering and hybrid filtering which combines aspects of both approaches [Balabanović, 97]. Content-based recommender systems match content resources to user interests, typically ...
A Comprehensive Context-Aware Recommender System Framework
Computer Science and Engineering—Theory and Applications, 2018
Context-Aware Recommender System research has realized that effective recommendations go beyond recommendation accuracy, thus research has paid more attention to human and context factors, as an opportunity to increase user satisfaction. Despite the strong tie between recommendation algorithms and the human and context data that feed them, both elements have been treated as separated research problems. This document introduces MoRe, a comprehensive software framework to build context-aware recommender systems. MoRe provides developers a set of state of the art recommendation algorithms for contextual and traditional recommendations covering the main recommendation techniques existing in the literature. MoRe also provides developers a generic data model structure that supports an extensive range of human, context and items factors that is designed and implemented following the object-oriented paradigm. MoRe saves developers the tasks of implementing recommendation algorithms, and creating a structure to support the information the system will require, proving concrete functionality, and at the same time is generic enough to allow developers adapt its features to fit specific project needs.
Using social data as context for making recommendations
Proceedings of the 1st Workshop on Context, Information and Ontologies - CIAO '09, 2009
Web-based knowledge systems support an impressive and growing amount of information. Among the difficulties faced by these systems is the problem of overwhelming the user with a vast amount of data, often referred to as information overload. The problem has escalated with the ever increasing issues of time constraints and the extensive use of handheld devices. The use of context is one possible way out helping with this situation. To provide a more robust approach to context gathering we propose the use of Social Web technologies alongside the Semantic Web. As the social web is heavily used it could provide a better understanding of a user's interests and intentions. The proposed system gathers information about users from their social web identities and enriches it with ontological knowledge. Thus an interest model for the user can be created which can serve as a good source of contextual knowledge. This work bridges the gap between the user and system searches by analyzing the virtual existence of a user and making interesting recommendations accordingly.
Context-aware recommender systems
Recommender Systems Handbook, 2011
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.
Context-Aware Recommender Systems: A Review of the Structure Research
2018
Recommender systems are a branch of retrieval systems and information matching, which through identifying the interests and requires of the user, help the users achieve the desired information or service through a massive selection of choices. In recent years, the recommender systems apply describing information in the terms of the user, such as location, time, and task, in order to produce relevant and even customized recommendations. Recently, some companies began to utilize the context information in their search engines. For instance, when choosing a song for the customer, it attempts to include the current mood of the listener in the context of the suggestions that the user makes. Employing context information, in view of the system's access and ability to collect information from the user interface, it offers more precise and user-friendly content that, in addition to obtaining user satisfaction, will also lead to the development and promotion of the field of work and the ...
Applied Sciences, 2017
Intelligent data handling techniques are beneficial for users; to store, process, analyze and access the vast amount of information produced by electronic and automated devices. The leading approach is to use recommender systems (RS) to extract relevant information from the vast amount of knowledge. However, early recommender systems emerged without the cognizance to contextualize information regarding users' recommendations. Considering the historical methodological limitations, Context-Aware Recommender Systems (CARS) are now deployed, which leverage contextual information in addition to the classical two-dimensional search processes, providing better-personalized user recommendations. This paper presents a review of recent developmental processes as a fountainhead for the research of a context-aware recommender system. This work contributes by taking an integrated approach to the complete CARS developmental process, unlike other review papers, which only address a specific aspect of the CARS process. First, an in-depth review is presented pertaining to the state-of-the-art and classified literature, considering the domain of the application models, filters, extraction and evaluation approaches. Second, viewpoints are presented relating to the extraction of literature with analysis on the merit and demerit of each, and the evolving processes between them. Finally, the outstanding challenges and opportunities for future research directions are highlighted.
The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context i...
Our current interest in personalisation is in making recommendations that are relevant in context. Our work is focused on SmartRadio, an internet-based music radio system. Music items in SmartRadio are organised into playlists. Users can create these playlists by selecting from available items and these playlists can be recommended to other users using collaborative (ACF) and similarity based recommendation [1]. The user provides explicit ratings to the system through the interface shown in Figure 1.
Problems and Opportunities in Context-Based Personalization
2011
ABSTRACT In a world of global networking, the increasing amount of heterogeneous information, available through a variety of channels, has made it difficult for users to find the information they need in the current situation, at the right level of detail.