Biblio

There are vast amounts of information in our world. Accessing the most accurate information in a speedy way is becoming more difficult and complicated. A lot of relevant information gets ignored which leads to much duplication of work and effort. The focuses tend to provide rapid and intelligent retrieval systems. Information retrieval (IR) is the process of searching for information that is related to some topics of interest. Due to the massive search results, the user will normally have difficulty in identifying the relevant ones. To alleviate this problem, a recommendation system is used. A recommendation system is a sort of filtering information system, which predicts the relevance of retrieved information to the user's needs according to some criteria. Hence, it can provide the user with the results that best fit their needs. The services provided through the web normally provide massive information about any requested item or service. An efficient recommendation system is required to classify this information result. A recommendation system can be further improved if augmented with a level of trust information. That is, recommendations are ranked according to their level of trust. In our research, we produced a recommendation system combined with an efficient level of trust system to guarantee that the posts, comments and feedbacks from users are trusted. We customized the concept of LoT (Level of Trust) [1] since it can cover medical, shopping and learning through social media. The proposed system TRS\_LoT provides trusted recommendations to the users with a high percentage of accuracy. Whereas a 300 post with more than 5000 comments from ``Amazon'' was selected to be used as a dataset, the experiment has been conducted by using same dataset based on ``post rating''.