the basic concept of neighbourhood for item-based recommendation comes from this paper:
http://portal.acm.org/citation.cfm?id=371920.372071 this is the idea: "The fact that we only need a small fraction of similar items to compute predictions leads us to an alternate model-based scheme. In this scheme, we retain only a small number of similar items. For each item j we compute the k most similar items. We term k as the model size. Based on this model building step, our prediction generation algorithm works as follows. For generating predictions for a user u on item i, our algorithm first retrieves the precomputed k most similar items corresponding to the target item i. Then it looks how many of those k items were purchased by the user u, based on this intersection then the prediction is computed using basic item-based collaborative filtering algorithm." -- View this message in context: http://old.nabble.com/item-based-recommendation-neighbourhood-size-tp27661482p27666954.html Sent from the Mahout User List mailing list archive at Nabble.com.
