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so in the last part of the lecture on recommender systems we will now see how we can combine latent vector models we also include the global effects that we talked about few videos ago right so the way we want to kind of model both the biases in interactions is in this kind of way right in some sense we would want to model the user bias we would want to model the movie bias and then you also want to model the user movie interaction right and the way we can achieve this is very similar to what we already talked about right we can have to in order to model biases we need we need kind of three values we need the overall mean rating of all the movies in our data set we need the bias of the user so how much does the user deviate on the average from the mean rating and how much does the movie deviate from the mean rating and then we will be using our latent factors to basically characterize the interaction between users and movies okay and the way we can do this is basically we want to put
