###### Abstract  
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. context-aware; extraction; evaluation; filtering; modeling; recommendation +
Footnote †: journal: Applied  
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values [3; 4]. The era of big data is also described as the era of data exploration and utilization [5], and the essential benefit from big data is not from the raw data itself but the insights and outputs 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].