# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 2 Related Works
### Content-Based Filtering  
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This type of recommendation approach is based on the analysis of the content of items that have been rated previously by the users and the content of items to be recommended [5]. These approaches aim to recommend similar items to those rated items by a user in the past. They can be used in different domains such as recommending restaurants, hotels, web pages, movies and journals. Many algorithms have been proposed for content-based filtering that predict a numeric value (i.e., _rating of an item_) show how far an item can interest a user.  
[PERSON] et al. [6] propose a content-based filtering approach for image recommendation. This approach utilizes affective metadata (i.e., metadata that describes emotions of the user, \"this image gives me a sad feeling\") and the content of an image to recommend. This work shows that the use of affective metadata in a content-based recommendation system for images yields a significant improvement in the performance of the recommendation much better than using generic metadata (e.g., genre).  
[PERSON] et al. [7] propose a content-based video recommendation system that includes a technique to analyze contents of videos and extract videos' features (i.e., low-level stylistic features such as lighting, color, shadows and camera motion). These features can be used to predict intended emotional, aesthetic or informative effects to provide users with recommendations. This technique neglects semantic features such as title of the film, year of production, nationality, genre of video, names of the actors, original language and subtitle. Despite that, the collection of high-level semantic features is more costly because it requires an editorial effort, which is not always available.  
To help authors retrieve the relevant journal and speed up the inscription and reviewing processes. [PERSON] et al. [8] propose a content-based recommendation system for journals or conferences publication known as Publication Recommendation System (PRS). This system can suggest the best conferences or journals based on the manuscript summary through a priority order process. The PSR uses two methods including Term Frequency and Inverse Document Frequency (TF-IDF) and chi-square feature selection. The first method TF-IDF offers a way to recognize the important terms of an article by associating a weight for each term [9]. While the second method chi-square statistic calculates the degree of relationships between the term \(t\) and a class \(c\), such as computer science journals or symposium [10].  
Further, the PRS adopts a real-time online system that uses a web tool to automatically update the training model. However, this system achieves 61.37% accuracy for paper recommendations, which is quite low. We believe that accuracy can be improved significantly if the system takes into account other criteria such as authors' preferences, impact factors, the first decision, and special issues.  
The major problem in the content-based filtering approach in recommendation systems is the overspecialization problem. In which the system provides recommendations based only on the user's profile and neglects the user's future interests. The system finds items close to those already recommended by the client. In other words, it does not provide novelty from the user perspective and prevents recommending surprising items that users have not discovered yet.