Elsevier

Knowledge-Based Systems

Volume 140, 15 January 2018, Pages 173-200
Knowledge-Based Systems

Characterizing context-aware recommender systems: A systematic literature review

https://doi.org/10.1016/j.knosys.2017.11.003Get rights and content

Abstract

Context-aware recommender systems leverage the value of recommendations by exploiting context information that affects user preferences and situations, with the goal of recommending items that are really relevant to changing user needs. Despite the importance of context-awareness in the recommender systems realm, researchers and practitioners lack guides that help them understand the state of the art and how to exploit context information to smarten up recommender systems. This paper presents the results of a comprehensive systematic literature review we conducted to survey context-aware recommenders and their mechanisms to exploit context information. The main contribution of this paper is a framework that characterizes context-aware recommendation processes in terms of: i) the recommendation techniques used at every stage of the process, ii) the techniques used to incorporate context, and iii) the stages of the process where context is integrated into the system. This systematic literature review provides a clear understanding about the integration of context into recommender systems, including context types more frequently used in the different application domains and validation mechanisms—explained in terms of the used datasets, properties, metrics, and evaluation protocols. The paper concludes with a set of research opportunities in this field.

Introduction

With the proliferation of big data & data analytics technologies, recommender systems (RS) are now crucial in seeking customer satisfaction through personalization [1]. RS aim at selecting and proposing the most relevant items, services and offers for their users, by considering their profiles, purchase history, preferences, opinions, interactions with offered products and services, as well as their relationships with other clients. At the same time, the generalization of smart-phones and ubiquitous computing has given RS access to context information [2]. Context-aware recommender systems (CARS) go one step further from traditional RS by exploiting context information such as time, location, and user activity to understand user situations and their influence on user preferences. The incorporation of context information into RS [2], [3] leverages the value of these systems by improving the relevance of possible recommendations with respect to changing user needs [4], [5].

The value of context information to improve the quality of recommendations has been demonstrated and supported by different researchers [6], [7], [8], [9], [10], [11]. Nevertheless, RS as well as context-awareness researchers and practitioners interested in combining the two areas still lack a guide that helps them understand how to exploit context information to smarten up RS. Evidence of this is the absence of comprehensive and domain-independent surveys, particularly systematic literature reviews, that not only consolidate the state of the art of the field, but also explain the most common techniques used to integrate context into the recommendation process. After a rigorous revision of the state of the art, we found that none of the available surveys comprehensively characterize recommendation processes from the perspective of the exploitation of context information. In the best cases, existing surveys focus only on the identification of used context types, and most of them address the problem from the perspective of a particular domain.

This paper presents the findings of a systematic literature review (SLR) [12] on CARS that we conducted with the goal of helping practitioners and researchers understand how context information can be effectively combined with recommendation mechanisms. To this end, we studied a final set of 87 CARS papers that were classified as content-based, collaborative filtering and hybrid approaches. For each paper, we identified recommendation techniques, means to exploit context information, context types, application domains, validation mechanisms including the used datasets, the improvements obtained through the exploitation of context (when measured quantitatively), and research opportunities. The main results of our study are reported in this paper in the form of a framework that characterizes recommendation processes in terms of: i) the recommendation techniques used at every stage of the process, ii) the techniques used to incorporate context, and iii) the stages of the process where context is integrated into the system. This manuscript aims at providing a clear understanding about where context information is usually integrated into the system, what techniques are available to exploit context information depending on the underlying recommendation approach and the phase of the process where context is included, what context types are more frequently exploited in the different application domains, and what validation mechanisms—explained in terms of the used datasets, properties, metrics and evaluation protocols—are generally used to evaluate the proposed approaches. Last, but not least, the paper discusses research opportunities relevant to CARS.

This paper is structured as follows. Section 2 explains foundational concepts on recommender systems and context information. Section 3 visits related work by analysing the contributions of our SLR with respect to other surveys published on CARS. Section 4 explains the methodology we followed to conduct the SLR. Sections 5–8 constitute the contributions of this manuscript: Section 5 presents the findings of our SLR and the characterization framework for CARS; Section 6 reports on the validation methods and datasets identified in the studied approaches; Section 7 presents quantitative data, reported in the studied papers, on the improvements obtained from the exploitation of context information; and Section 8 summarizes and classifies research opportunities. Finally, Section 9 concludes the paper.

Section snippets

Background

This section briefly presents the fundamentals of RS, and context information as an enabler to improve the quality of recommendations.

Related work

We found 15 RS surveys published in relevant venues and journals between 2004 and 2016. However, only 7 out of these 15 surveys, published between 2012 and 2014, relate to the improvement of RS through the incorporation of context information. Aiming at providing a comprehensive understanding of the state of the art of this field, our SLR not only follows a well defined research methodology, but also characterizes CARS along all application domains, context types, and techniques reported in the

Methodological aspects

We conducted this study by following the guidelines proposed by Kitchenham and Charters in [12]. With our long-term research goal in mind—to look for innovative and more effective ways of exploiting context information to improve the effectiveness of recommender systems, we defined the set of research questions that would allow us to understand the state of the art of CARS. These questions are stated as follows:

  • RQ1: How is context information exploited along the recommendation process?

  • RQ2:

Characterization of Context-Aware RS (CARS)

This section summarizes, for each type of recommender system, the findings of our SLR. We consider that the differences between content-based, collaborative filtering, and hybrid recommenders are too profound to analyze them all together, thus we set to do it independently.

To characterize content-based and collaborative filtering CARS, we first represented their recommendation processes using flow diagrams (cf. Figs. 1 and 2) that allow us to distinguish the different phases they comprise, and

Characterization of validation methods

The improvement of user experience is the ultimate goal of a recommender system. In order to measure it, a series of properties, each with a set of metrics, have been proposed and used since the first developments in the field. These properties allow us to determine the pertinence of the recommendations being suggested. Instances of these properties are predictive power, confidence, diversity, learning rate, coverage, scalability and user evaluation [111].

In this section we summarize the

The effect of incorporating context into RS

When conducting an SRL on CARS, a natural question is the level of improvement of RS performance (e.g., in terms of accuracy) obtained from the inclusion of a particular context type into the recommendation process. Nevertheless, answering this question results impractical, given the wide spectrum of recommendation techniques that can be combined with the different context types, through any of the three existing paradigms to include context information into RS. Furthermore, the performance of

Research opportunities

This section provides CARS researchers with a list of research opportunities, most of them borrowed from the studied articles. From each paper, we identified, categorized, and analyzed the challenges that authors defined as worthy of future work. Each subsection corresponds to one of the nine challenge categories that we identified: dynamic context management, context gathering, context reasoning, contextual modeling, problems inherent in RS, CARS evaluation, users in the loop, self-adaptation

Conclusions

This paper presented a comprehensive characterization of context-aware recommendation processes and systems, based on the findings of a systematic literature review (SLR) we conducted to survey CARS that were published between 2004 and 2016. This study was conducted with the goal of helping practitioners and researchers understand how context information can be effectively combined with recommendation mechanisms. The main results provide a clear understanding about where context information is

Acknowledgment

This work was funded by Universidad Icesi through its institutional research support program.

References (114)

  • M.A. Domingues et al.

    Dimensions as virtual items: Improving the predictive ability of top n recommender systems

    Inf. Process. Manage.

    (2013)
  • T.H. Dao et al.

    A novel recommendation model of location-based advertising: Context-aware collaborative filtering using ga approach

    Expert Syst. Appl.

    (2012)
  • HongW. et al.

    Product recommendation with temporal dynamics

    Expert Syst. Appl.

    (2012)
  • WangW. et al.

    Member contribution-based group recommender system

    Decision Supp. Syst.

    (2016)
  • ZhengL. et al.

    Context neighbor recommender: Integrating contexts via neighbors for recommendations

    Inf. Sci.

    (2017)
  • ZhangJ.-D. et al.

    Core: exploiting the personalized influence of two-dimensional geographic coordinates for location recommendations

    Inf. Sci.

    (2015)
  • A. Nocera et al.

    An approach to providing a user of a social folksonomy with recommendations of similar users and potentially interesting resources

    Knowl. Based Syst.

    (2011)
  • ZhengN. et al.

    A recommender system based on tag and time information for social tagging systems

    Expert Syst. Appl.

    (2011)
  • S.-H. Min et al.

    Detection of the customer time-variant pattern for improving recommender systems

    Expert Syst. Appl.

    (2005)
  • ZouB. et al.

    GPUTENSOR: efficient tensor factorization for context-aware recommendations

    Inf. Sci.

    (2015)
  • P. Do et al.

    A context-aware collaborative filtering algorithm through identifying similar preference trends in different contextual information

    Advanced in Computer Science and its Applications

    (2014)
  • M. Zhang et al.

    Addressing cold start in recommender systems: a semi-supervised co-training algorithm

    Proc. 37th ACM SIGIR Int. Conf. on Research & development in Information Retrieval

    (2014)
  • P. Sitkrongwong et al.

    Bayesian probabilistic model for context-aware recommendations

    Proc. 17th Int. Conf. on Information Integration and Web-based Applications & Services

    (2015)
  • X. Ramirez-Garcia et al.

    Post-filtering for a restaurant context-aware recommender system

    Recent Advances on Hybrid Approaches for Designing Intelligent Systems

    (2014)
  • F. Ricci et al.

    Recommender Systems Handbook

    (2011)
  • G.D. Abowd et al.

    Towards a better understanding of context and context-awareness

    Handheld and Ubiquitous Computing

    (1999)
  • N.M. Villegas

    Context Management and Self-Adaptivity for Situation-Aware Smart Software Systems

    (2013)
  • G. Adomavicius et al.

    Incorporating contextual information in recommender systems using a multidimensional approach

    ACM Trans. Inf. Syst.

    (2005)
  • N.M. Villegas et al.

    Managing dynamic context to optimize smart interactions and services

    The Smart Internet

    (2010)
  • G. Adomavicius et al.

    Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

    IEEE Trans. Knowl. Data Eng.

    (2005)
  • S. Ebrahimi et al.

    SmarterDeals: a context-aware deal recommendation system based on the SmarterContext engine

    Proc. 2012 Conf. of the Center for Advanced Studies on Collaborative Research

    (2012)
  • G. Adomavicius et al.

    Context-aware recommender systems

    Recommender Systems Handbook

    (2011)
  • H. Ma et al.

    Improving recommender systems by incorporating social contextual information

    ACM Trans. Inf. Syst.

    (2011)
  • U. Panniello et al.

    Incorporating context into recommender systems: an empirical comparison of context-based approaches

    Electron. Commerce Res.

    (2012)
  • B. Kitchenham et al.

    Guidelines for performing Systematic Literature Reviews in Software Engineering

    Technical Report

    (2007)
  • B. Sheth et al.

    Evolving agents for personalized information filtering

    Proc. 9th Conf. on Artificial Intelligence for Applications

    (1993)
  • M. Pazzani et al.

    Learning and revising user profiles: the identification of interesting web sites

    Mach.Learn.

    (1997)
  • W. Hill et al.

    Recommending and evaluating choices in a virtual community of use

    Proc. SIGCHI Conf. on Human Factors in Computing Systems

    (1995)
  • P. Resnick et al.

    Grouplens: an open architecture for collaborative filtering of netnews

    Proc. 1994 ACM Conf. on Computer supported cooperative work

    (1994)
  • R. Burke

    Knowledge-based recommender systems

    Encyclopedia of Library and Information Systems

    (2000)
  • K. Verbert et al.

    Context-aware recommender systems for learning: a survey and future challenges

    IEEE Trans. Learn. Technol.

    (2012)
  • A. Zimmermann et al.

    An operational definition of context

    Modeling and Using Context

    (2007)
  • LiuQ. et al.

    A survey of context-aware mobile recommendations

    Int. J. Information Technol. Decis. Mak.

    (2013)
  • P.G. Campos et al.

    Temporal rating habits: a valuable tool for rating discrimination

    Proc. of the 2nd Challenge on Context-Aware Movie Recommendation

    (2011)
  • S. Inzunza et al.

    User and context information in context-aware recommender systems: a systematic literature review

    New Advances in Information Systems and Technologies

    (2016)
  • S. Seifu et al.

    A comprehensive literature survey of context-aware recommender systems

    Int. J. Adv. Res. Comput. Sci. Softw. Eng.

    (2016)
  • A.M. Otebolaku et al.

    Context-aware media recommendations for smart devices

    J. Ambient Intell. Hum. Comput.

    (2015)
  • C. Musto et al.

    Contextual eVSM: a content-based context-aware recommendation framework based on distributional semantics

    E-Commerce and Web Technologies

    (2013)
  • C. Biancalana et al.

    An approach to social recommendation for context-aware mobile services

    ACM Trans. Intell. Syst. Technol.

    (2013)
  • CaoB. et al.

    Mashup service recommendation based on user interest and social network

    2013 IEEE 20th International Conference on Web Services

    (2013)
  • Cited by (0)

    View full text