## 3 Classification of the Reviewed Literature
### Application Domain and Their Incorporated Contexts
#### (2) e-Documents Domain  
One generic concentration of the reviewed literature is the migration of learning recommenders towards mobile platforms. Recommender systems play important roles in the educational domain, where information relating to researchers are proactively recommended. Due to the high acceptability of mobile phones and their ease of use, researchers are migrating educational RS into mobile platforms. However, the mechanism must be able to sense both the learner-centric and system-centric contextual information and adapts accordingly the accessed services at run-time. The authors of [61] proposed a light-weight mobile RS with semantic-rich awareness information, which is able to provide content recommendations that are tailored to learners' background, contexts and the task at hand. In [62], an assessment of the usefulness of contextual information with respect to classical RS was performed to determine users' utilization of mobile news services and how contextual information influencestheir consumption behavior. While [61] and [62] suggest new mechanisms in mobile RS, [63] report a framework designed for library staff to provide location-based access to information via mobile computing platform, which eases access and utilization of the library collection.  
Another critical observation is the use of a collaborative approach instead of the well-known content-based approach in an educational domain. A context-based collaborative framework that uses only easily obtained citations relations as source data was proposed in [64]. The framework employs an association-mining technique to obtain a paper representation of the paper citation context. A pairwise comparison was then performed to compute the extent of similarities between papers. The authors of [65], hypothesized the author's previous publications to contained a clean signal of the researcher's latent interests. The key aspect of their model was to enrich the user profile with the information coming directly from the references of the researcher's previous publications as well as the papers that cited them. Whereas the authors of [65], recommend papers relevant to the researcher's interest, they also addressed the serendipitous scholarly paper recommendation in [66]. Conversely, the authors of [67] proposed a collaborative approach for research paper recommender system to identify the latent associations that exist between research papers based on the perspective of paper-citation relations.  
Some of the reviewed literature have also proposed some solutions to the sparsity and cold start problems in the educational domain. To be precise, the authors of [68] claimed to proposed a scalable large-scale context-aware recommender system that does not suffer from cold start problems. The approach uses an adaptive item clustering algorithm to address the cold start problem and improve the learning speed. On the other hand, authors of [69] proposed a context-aware recommendation model that integrates convolutional neural network (CNN) into probabilistic matrix factorization (PMF) to capture the contextual information of a document to enhance the rating accuracy. The authors of [70] have also introduced a flexible architecture using neural citation network (NCN) capable of incorporating author metadata for context-aware citation recommendation.  
Another important aspect of the literature is the issue of institution-industry collaborations, which play an important role in the success of research and development (R&D) projects. The authors of [71] proposed a context-aware researcher recommendation system to encourage university-industry collaboration on industrial R&D projects. The benefits of supporting learners with effective educational recommendations have been presented in [72], and the requirements for the realization of e-learning full potentials has also been outlined in [73].