# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 2 Related Works
### Hybrid Filtering  
As we have shown above, each collaborative and content-based technique has its limitations, like the overspecialization, the clod start and the sparsity problem. In fact, hybrid approaches have been proposed to enhance the recommendation accuracy and avoid above-mentioned limitations of collaborative and content-based techniques. [PERSON] et al. [21] propose a mixture recommendation technique that depends on both collaborative filtering and content-based filtering to get out the best of both approaches. Content-basedfiltering is based on the analysis of the content of past relevant documents to construct user profiles. Furthermore, CF is based on the correlation of users' ratings of past items to recommend novel items where a correlation coefficient is calculated between each user. However, this work neglects the contextual aspect of users such as spatiotemporal-based and role-based user preferences, which reduces the quality of recommendation. [PERSON] et al. [22] develop a platform called a Context-aware Media Recommendation (CoMeR) based on hybrid-processing approach. It combines three different approaches, including, the content-based approach, Bayesian-classifier approach and the rule-based approach. Furthermore, it employs a context-aware model that represents the user profile including, spatiotemporal information, and user preferences. This platform supports media adaptation and recommendations for smart mobile applications. This work shows significant results with accuracy rates around 0.8 and recall rates around 0.75. However, the CoMeR platform lacks flexibility. It provides a static model in which the database is updated in an offline manner. Therefore, we intend to fix this problem by using a learning process classifier that updates automatically and dynamically the database in an online manner. [PERSON] et al. [23] develop a hybrid method using multi-criteria-based collaborative filtering for hotels recommendation. Further, to improve the recommendation accuracy of multi-criteria CF, they combined three techniques, including, Expectation-Maximization (EM) algorithm, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Principal Component Analysis (PCA) for dimensionality reduction. This work shows high accuracy on the TripAdvisor dataset. However, items' model and users' model are updated in an offline manner, which makes this work incapable to incrementally adapt new data ratings. Therefore, incremental learning is needed to consider new updates on the fly. Services recommendation becomes an important aspect in pervasive environments due to their dynamicity and heterogeneity. Various recommendation approaches that we have studied in previous sections focus only on rating and similarity measurement and neglect context information. Neglecting the context in recommendation systems remains inappropriate for such dynamic environments (i.e., context changing). Therefore, it will be necessary to consider context-aware approaches in recommendation systems to recommend more relevant services for the user's needs and usage context.