## 3 Classification of the Reviewed Literature
### Application Domain and Their Incorporated Contexts
#### 1.1.1 E-commerce Domain  
It is a fact that the WWW brings more competition to the market environment, which leads to the evolution of many paradigms targeting effective marketing strategies. One of the most common and obviously the most successful is the C2C (Consumer-to-Consumer) paradigm. C2C is an e-commerce paradigm that allows a single consumer to play both the roles of a buyer and a seller for different transactions. However, the approach manages objects arrangement and product catalogue using lexicographical descriptions, which are prone to returning irrelevant results because of polysemy of words, and its inability to use current technology for automatic comparisons across product recommendations, while the approach is suitable for C2C, its inappropriate for a direct shopping paradigm such as B2C (Business-to-Consumer) e-commerce. In bridging the gap, the authors of [54] proposed a B2C paradigm that hierarchically arranges products according to their features and use a product selection system to search and return another list of relevant products to be presented to the users as the set of recommendations. Whereas the authors of [54] consider a generic B2C scenario, the authors of [55] consider a scenario where the consumers can be physically present at the shopping small popularly known as In-Store-Shopping (ISS). On another development, the idea of segmenting customers into possibly non-overlapping segments has been proposed in [56] to adapt the quick changes of customer's preferences. The authors identified three different approaches to include contexts in a segmentation model for building an effective predictive model of customer behavior for e-commerce recommendations. A detailed comparison is conducted across these approaches in [37]. In addition to considering predictive  
Figure 3: Classification by domain of application. accuracy, the comparison also considers the diversity of recommendations. The result of that marginal and regional analysis reveals that no single approach is better than the other in all cases. However, some approaches are better in certain circumstances. Further important advancement from the extracted literature is the attempt to solve the well-known problem of sparsity and cold start in e-commerce. As a matter of fact, e-commerce recommenders utilize ratings from several users to provide personalized and qualitative recommendations. This approach hinders the effectiveness of RS as it contributes to sparsity and cold start problems. The latter problem is the situation where the total ratings of a user are not sufficient for qualitative recommendations. Whereas, the former problem is the difficulty to find a sufficient and reliable number of users so that system can make reliable and qualitative recommendations. Authors of [57] presented a conceptual model called _prior ratings_ to source ratings information from e-commerce users. The approach was built upon a virtual experience to motivate users to provide ratings, even users that do not purchase an item are eligible to rate it. Users' opinions about products are captured from their interactions with virtual products represented in a mediated environment. While previous researches use personalized recommendations based on users' historical behavior [58], two concerns are raised in [59]. Firstly, those approaches do not consider users' intentions, which is crucial because despite having the same searching and browsing behavior, users might have different considerations and secondly, the user's budget is an important constraint for shopping. With this in mind, the authors of [59] proposed a novel context-aware e-commerce that tracks user's real-time shopping context to provide an autonomous mechanism for enhancing RS performance. Recommendations adapted to these considerations might be more interesting for users and RS performance might have improved if recommendations consider users' budget.