## 4 MAS-Based Prevention and Recommendation Systems Review
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Problem to Address** & **Technique** & **Contributions of the Proposal** \\ \hline \multirow{6}{*}{[43]} & Content-based recommendation system, aiming to solve the new user and overspecialization problems. & MAS architecture as a recommendation system. Semantic enhancement of user preference through domain ontology and semantic association discovery in user profile database. & Addresses two existing problems in an existing technique, and experimental results show an improvement in positive feedback rate. \\ \hline \multirow{6}{*}{[44]} & \multirow{6}{*}{Group recommendation.} & MAS approach based on negotiation techniques. A multilateral monotonic concession protocol is used to combine individual recommendations into a group recommendation. & Implementation of group recommendation using a MAS architecture and a multilateral protocol. Testing the proposed approach in the movies domain, users were found more evenly satisfied in the groups than with ranking aggregation. \\ \hline \multirow{6}{*}{[45]} & \multirow{6}{*}{Group recommendation.} & Social-emotional model computed using an ANN, based on the pleasure, arousal, and dominance (PAD) three-dimensional emotional space. Application of the model in a group of Human-Immersed agents. & Artificial Neural Network (ANN) that computes the social emotion of a group of agents. Experiments show that using the proposed model to predict the emotion of a group of agents and computing the distance to a target emotion \\ \cline{1-1}  & \multirow{6}{*}{Cyber-bullying and online grooming prevention in SNSs through the use of different techniques.} & Sentiment analysis on text by using different text mining modules, adult image detection using Skin Tone Pixels detection and message classification using Natural Language Processing (NLP) algorithms, through keyword search in the text. & Combination of different data analysis techniques including text and image analysis for prevention of negative outcomes in SNSs. Experiments with data from Twitter that show significant differences between the analyzers predicting negative outcomes, and with a real-life SNS. \\ \hline \multirow{6}{*}{[47]} & \multirow{6}{*}{Prevention of negative outcomes in SNSs, negative sentiment, and high stress levels through decision-level fusion of sentiment and stress analysis on text with new sentiment, and high stress levels through decision-level fusion of naturalness on text.} & \multicolumn{1}{p{113.8 pt}}{} & \multicolumn{1}{p{113.8 pt}}{} \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._In Reference [38] agents and a multi-agent system are suggested to work as communicator mediators between users of SNSs and social groups; in Reference [39] an ontology is constructed by monitoring user behavior, and later used in a task of collaborative filtering recommendation, by means of computing inter-ontology similarities; trust and reputation of agents in a MAS architecture are computed in Reference [40] on the basis of certified recommendations (e.g., based on signed or witnessed transactions), to make the system able to determine how much the agents can be trusted as experts; in Reference [41] an XML-based MAS architecture is proposed, which the authors called MAST. It supports business-to-customer (B2C) e-commerce activities, by means of user personalized profiles that are built and updated by weighting the activities performed in B2C processes. Mas architectures can be useful for several different purposes.