By increase in the amount of movies offered through the web, people require a mean to find and evaluate these many alternatives. Recommender systems are a solution to this problem. Actually, recommendation systems are emerging as an important business application with significant economic impact. Movie recommendation systems such as Netflix or our proposed system (MovieIt) recommend movies to users. In fact, recommender systems help consumers, to find out about new products, and this increases sales diversity. Providing these kinds of systems is an incentive for movie companies, because it can help them increase profits.
Some believe that the likelihood of purchasing a recommended product depends on the information provided, context and familiarity. In MovieIt recommender system, people receive recommendations from their Facebook friends. In other word, it is more likely that the recommendation they get from their friends, matches their taste. On the other hand, with MovieIt people can also rate a movie and write a comment about the movie they have seen. This means, that other than giving recommendation, some other information is also provided about those movies. This makes the process of decision for users much easier. All of this can increase the probability of purchasing a movie or going to the cinema and watching it which causes more profit for the movie company.
These kinds of applications can revolutionise the way we choose products. Furthermore, these types of applications can leverage the power of social interactions and use it for making more informed, more suitable and simply better decisions.