Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction (original) (raw)
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Progress in context-aware recommender systems — An overview
Computer Science Review, 2019
Recommender Systems are the set of tools and techniques to provide useful recommendations and suggestions to the users to help them in the decision-making process for choosing the right products or services. The recommender systems tailored to leverage contextual information (such as location, time, companion or such) in the recommendation process are called context-aware recommender systems. This paper presents a review on the continual development of context-aware recommender systems by analyzing different kinds of contexts without limiting to any specific application domain. First, an in-depth analysis is conducted on different recommendation algorithms used in context-aware recommender systems. Then this information is used to find out that how these techniques deals with the curse of dimensionality, which is an inherent issue in such systems. Since contexts are primarily based on users' activity patterns that leads to the development of personalized recommendation services for the users. Thus, this paper also presents a review on how this contextual information is represented (either explicitly or implicitly) in the recommendation process. We also presented a list of datasets and evaluation metrics used in the setting of CARS. We tried to highlight that how algorithmic approaches used in CARS differ from those of conventional RS. In that, we presented what modification or additions are being applied on the top of conventional recommendation approaches to produce context-aware recommendations. Finally, the outstanding challenges and research opportunities are presented in front of the research community for analysis
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 ...
Context-Aware Recommender Systems: A Comparison Of Three Approaches
Methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. This paper proposes a novel type of contextual modeling, that is called contextual neighbors, based on the idea of using context to compute the neighborhood in a collaborative filtering approach, and introduces four variants of this method. In addition, the paper presents the results of the comparison among these four approaches and among the contextual neighbors approach to the other contextual approaches and to the un-contextual one. While some of these methods have been studied independently, few prior research has compared their performance to determine which of them is better.
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 ...
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.
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.
Context-Aware Recommender System Frameworks, Techniques, and Applications: A Survey
2019 International Conference on Innovative Trends in Computer Engineering (ITCE’2019), 2019
Tailored services or information for mobile users became very crucial; due to the enormous amount of information and services that are available for mobile users during their various activities. Recommendation system can be merged with context awareness to provide tailored services, information, moreover learning resources for users, based on their current context and profile. Context Aware Recommendation Systems (CARSs) have various usages also many applications and services depend on it, CARSs nowadays become widely used in various domains. This paper presents a survey of CARSs overview, frameworks, techniques, algorithms, and applications. Moreover, the paper discusses the issues of CARSs in general and investigates the proposed solutions.
Journal of Computer Science
Recommender systems are being widely applied in many fields, such as e-commerce, e-documents, places and travel, multimedia, news and advertising and transportation. These systems are similar to an information filtering system that helps to identify a set of items that best satisfy the users' demands based on their preference profiles. The integration of contextual information (e.g., location, weather conditions and user mood) into recommender systems to improve their performance has recently received considerable attention in the research literature. Studies in the relevant literature have focused on incorporating contextual information into conventional recommender systems by employing three approaches: Pre-filtering, post-filtering and modeling. In this paper, we conduct a systematic comparison of various approaches and show how to integrate contextual information into recommender systems. Additionally, we provide an in-depth analysis of the most notable studies to date and point out the strengths, weaknesses and application scenarios for each of the approaches. We also empirically evaluate the real-world datasets, analyzing distinct recommendation quality metrics and characteristics of the datasets. An important result is that accuracy-based comparisons show no clear winner among the approaches.
Incorporating context into recommender systems: an empirical comparison of context-based approaches
Electronic Commerce Research
Recently, there has been growing interest in recommender systems (RSs) and particularly in context-aware RSs. Methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. This paper focuses on comparing the pre-filtering, the post-filtering, the contextual modeling and the un-contextual approaches and on identifying which method dominates the others and under which circumstances. Although some of these methods have been studied independently, no prior research compared the relative performance to determine which of them is better. This paper proposes an effective method of comparing the three methods to incorporate context and selecting the best alternatives. As a result, it provides analysts with a practical suggestion on how to pick a good approach in an effective manner to improve the performance of a context-aware recommender system.
Domain of application in context-aware recommender systems: a review
2016
The purpose of this research is to provide an exhaustive overview of the existing literature on the domain of applications in recommender systems with their incorporated contextual information in order to provide insight and future directions to practitioners and researchers.We reviewed published journals and conference proceedings papers from 2010 to 2016.The review finds that multimedia and e-commerce are the most focused domains of applications and that contextual information can be grouped into static, spatial and temporal contexts.