Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

Khalid Haruna, Maizatul Akmar Ismail, Suhendroyono Suhendroyono, Damiasih Damiasih, Adi Pierewan, Haruna Chiroma, Tutut Herawan
2017 Applied Sciences  
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
more » ... ecommender 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. that emerge from processing and analyzing it [6, 7] . Therefore, the boundless changes in big data from a management and technical approach need to be accompanied by intelligent techniques and applications that can properly and intelligently store, process, access and analyze information for maximum user benefit [8] [9] [10] [11] . Recommender systems (RS) are at leading edge of systems available for users to leverage relevant information from the vast amount of information [12] and are emerging as appropriate tools to aid and speed up the process of information seeking, considering the dramatic increase in big data. In its broadest definition, an RS is a software tool and technique that provides the best suggestions for items and services to users, typically from a large information space. Suggestions are based on the user's interests and preferences among different alternatives and then those items and services are presented to the user in a suitable manner. RS are of great importance in facilitating the process of decision making, which leads to the development of many real-world applications [13] . Its applicability can be evidenced by its acceptability across various sectors in e-government [14] [15] [16] , e-business [17,18], e-library [19,20], e-commerce/shopping [21,22], e-learning [23,24], e-tourism [25,26], e-resource services [27,28], e-group activities [29, 30] , and etc. It has also been utilized to improve humans' perceived quality of experience [31] [32] [33] . RS systems are concerned with data collection, pre-processing, transmission, data storage and extracting information using statistical and analytical methods. They emerged in the 1990s and 2-dimensional approaches (users X items) were predominantly used to predict users' interests [34] . These approaches utilized user-defined items as a set of entities to project the ratings that are either implicitly inferred by the system [35] or are explicitly provided by the users [36] . However, in the early 2000s, RS researchers extended the research in RS to leverage contextual information in addition to the classical two-dimensional process in order to provide better-personalized recommendations to users [37] . Several works exist on context-aware recommendation system (CARS) and researchers are becoming more interested in this field since its emergence [38] , but few papers can be traced that have undertaken a rigorous systematic literature review to analyze the complete CARS developmental process. To be specific, the authors of [39], proposed the architectural principles for generic context-aware recommendation systems and derived a layered framework to ease the development of CARS applications. The authors of [40] have also conducted a review of the generic context-aware recommendation systems to suggest a new classification framework. Their proposed classification was based on a five-layered architecture of context-aware systems to help researchers in extracting important lessons for the implementation of CARS systems. A number of existing context-aware systems have been surveyed in [41] . The survey outlined and explained the general processes and the design considerations in context-aware systems. While the authors of [42], examined and classified 210 RS papers into their various domain of applications and their different data mining techniques. A survey on Technology Enhanced Learning (TEL) has also been conducted in [43] . The survey identified and analyzed the contextual dimensions for the development of CARS for TEL. A general overview on RS together with collaborative filtering methods and algorithms has been detailed out in [44] . The overview provides original classifications for RS systems and identifies areas for future implementations. In addition, a review has been conducted in [45] , to determine the contexts and the methods used for making recommendations in digital libraries. The authors of [46] presented a systematic literature review on CARS to identify the contextual information considered relevant by the researchers in generating context-based recommendations from 2012 to 2015. The authors of [47] have also presented a comprehensive overview of context-aware systems in a mobile environment to identify the contextual information that has been used in CARS in , 7, 1211 3 of 25 order to sketch the possible future directions. The review has categorized the contextual information into six main categories and asserted that accumulating too many contextual factors can negatively affect the recommendation quality and leading to scalability problems. As can be observed, each of the above research has addressed a specific aspect of the CARS process. Unlike the aforementioned works, this paper performs an integral approach to the complete CARS developmental process. By so doing, it aims to fill the gap and provide both the novice and new researchers the required background knowledge behind each process step. An in-depth review is presented on the state-of-the-art of CARS and the literature are classified based on the domain of their application, model, filter, extraction, and evaluation approaches. Then microscopic views are presented on the extracted literature by analyzing the merit and demerit of each and the evolving processes between them. In this way, the outstanding challenges are highlighted including the opportunities for future research directions. In summary, the contributions of this paper are: a. The previous CARS researches are categorized according to their domain of application, modeling, filtering, extraction, and evaluation approaches. b. The review explores the different contextual information incorporated in recommender systems and our rigorous analysis has demonstrated that they could be categorized into spatial, temporal and static. c. The review has analyzed the merit and demerit of previous CARS researches and their evolvement processes. d. The paper also presents outstanding challenges and suggests possible opportunities for future research directions. The remaining sections of the paper are organized as follows. Section 2 presents the adopted method for extracting the literature. In Section 3, a coherent classification is presented and microscopic analysis of the reviewed literature. Recommendations and future research directions in Section 4. We then conclude the paper in Section 5. Methodology Identification of Bibliographical Databases Eight (8) major computer science bibliographical databases were identified and selected; these databases are ScienceDirect, ACM Digital Library, SpringerLink, Web of Science, IEEE Xplore, Scopus, DBLP computer science bibliography, and Google Scholar portal. As stated in [48] , the dates for starting and closing a
doi:10.3390/app7121211 fatcat:p43pqjs7lbdcbknbo76anzawui