# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 2 Related Works
### Context-Aware Recommendation System  
Recently context has been widely used in multiple disciplines [10]. For instance, classic recommendation systems can take advantage of contextual information such as location, time, user activity and social information, to improve recommendation accuracy. The integration of context information has been adopted by many researchers in many areas including pervasive environments, marketing, smart domains and IoT [10]. Some researchers exploit the time and location information to reduce the search space that enables to enhance recommendation accuracy, namely, time-aware and location-aware recommendation [24; 25]. Point-Of-Interest (POI) recommendation is one of the most context-aware recommendation systems that is based on spatiotemporal context information. It recommends the best places to visit. [PERSON] et al. [15] introduce a spatiotemporal-aware POI recommendation system to recommend a list of POIs for a given user's location at a specific time, e.g., recommend for a user to visit a nearby restaurant at midday. They develop a POI recommendation model based on human behaviors, namely, temporal behaviors and spatial behavior. Moreover, temporal and spatial behavior can play a significant role in analyzing users' activities and improving recommendation accuracy. For instance, if many users visit a restaurant at midday but very few users visit a library at the same time, then the restaurant should be given a higher priority than the library during recommending POIs for a user to visit at midday. However, this work tends to produce a pool of unrelated POIs that the user may not continuously visit due to the lack of related associations between POIs. To cope with that issue, [PERSON] et al. [16] propose a recommendation system based on user-effective Point-of-Interest path (POI) that can recommend POIs by considering both possible associations and diversity features of POIs. In that case, they develop a top-k POIrecommendation model based on effective path coverage to improve the performance of the recommendation algorithm. People in real life tend to seek advice from their entourage based on social context before trying or purchasing something new. Integrating social context in recommendation systems increase significantly the performance of the recommendation in terms of accuracy. Researchers have used social information (e.g., family, followed and followers, friends list, relationships, trusted and untrusted users) to improve the prediction when providing recommendations. [PERSON] et al. [17] propose context-aware recommendations using a role-based trust network. They use the term _role_ to model common context-aware interests within a group of users. The user can play different roles (e.g., diner, movie fan and reader), which may change dynamically by context changes. They use Weighted Set Similarity Query (WSSQ) algorithm to calculate the value of trust between two users in order to build the user's role-based trust network in a given context. The recommendation is made based on both calculated user's role-based trust network and user-item rating matrix. However, user context and role sets are predefined in an offline manner, which makes this work incapable to incrementally adapt to new context users. [PERSON] et al. [18] propose a personalized recommendation system based on friendship strength using Twitter as a source of big Social Networks Systems (SNS). It recommends interests to users by considering various characteristics of big SNS. This approach is based on social information for measuring closeness between users that are defined as friendship strength. Friendship strength is calculated using three categories of social data, including contents generated by users, relationships information and interaction information. More precisely, three similarities are calculated for each social data category, including personal, group and interaction similarity, where friendship is the combination of these three similarities.