# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 2 Related Works
### Collaborative-Filtering (CF)  
These approaches are based on aggregating and analyzing users' experiences in the past, then predicting users' interests in the future. As collaborative filtering approaches rely on users' feedback, they can recommend accurately complex items (e.g., video or music content) than content-based filtering. However, the extraction of visual features of videos is quite difficult. In addition, suggesting appropriate items to a client based on their interests and desires extracted from their feedbacks appears naturally easier. One of the major issues in CF is the sparsity of users' ratings.  
To address this issue, [PERSON] et al. [11] present a CF recommendation system based on a slope smoothing scheme and items classification. This approach predicts unrated items by employing two main phases to produce recommendations. The first step calculates the average deviation of two items based on slope one scheme. The second step generates the prediction based on Pearson correlation similarity.  
[PERSON] et al. [12] propose a CF technique based on Bayesian probabilistic model to predict the tastes of users. This technique adapts the factorization of high-rating matrix. Unlike the traditional factorization of matrix, this mechanism assigns a vector k ranging from [0, 1] with an understandable probabilistic model which allows identifying classes of users having similar desires. The new factorization technique offers a significant improvement in terms of quality of prediction and recommendation accuracy. However, this technique imposes a significant computational time during the decomposition of the initial matrix into two matrices.  
Recently, deep learning-based methods find a way to submerge in recommendation systems. [PERSON] et al. [13] propose a Neural Graph Collaborative Filtering (NGCF) based recommendation system. This system includes user-item interaction graph into high-order connectivity graph-based embedding function preserving collaborative signal. However, the user-item interaction structure used for understanding user behaviors is quite poor and based only on collaborative signal. More specifically, many other forms of knowledge models can be used to better understand user behavior, such as context-aware model [14, 15, 16] and social networks [17]. So far, several collaborative filtering recommendation systems have been studied and tested.  
We found that collaborative filtering approaches could address one problem at once either overcoming the cold start (i.e., when a new user has not provided enough ratings, or a new item has not been rated yet, the system is not able to generate reliable recommendations) or augmenting the recommendation accuracy rate. Furthermore, the cold start problem consists of rating data sparsity. The data sparsity evaluates the ratio of available ratings to all possible ratings [18]. In real recommendation systems, the sparsity is very close to one (100%) since users rate a small number of database records [19]. For these causes, it is difficult for collaborative filtering to provide good results addressing these problems. To deal with these problems, researchers use hybrid recommendation techniques where they combine two or more mechanisms, e.g., combine collaborative and content-based techniques [20].