This is an article presenting a website, FilmTrust, for rating and recommending movies to the users. The most important idea that we can get for Shopster out of this article is the way they propose for calculating user trust in the system. Each user is able to assign a trust value to their friends (e.g. Alice trusts Bob 9 out of 10) and therefore their recommended rating for a movie can be predicted by observing their most trusted friends’ rating, using TidalTrust.
For example if Alice trusts Bob 9 and Mary 3, and if Bob rated a movie with 4 stars while Mary rated the same movie with 2 stars, the recommended rating for Alice would be: [(9 x 3) + (3 x 2)] / (9 + 3) = 3.5. This method was also compared to a recommended rating using Pearson Correlation coefficient (Automatic Collaborative Filtering) for calculating their nearest neighbour’s prediction as well as a simple total average for a movie, and found to be more accurate than both of them.
We could use this method of user trust between users in Shopster with the only drawback being that this method is not efficient if a user does not have any friends.
Article source: FilmTrust: Movie Recommendations using Trust in Web-based Social Networks