Weight Adjusment for Multi-criteria Ratings in Items Recommendation
Felipe Born de Jesus, Carina F. Dorneles · 2016
In this paper we propose to use implicit ratings of multiple criteria to mitigate the data sparsity problem. The intuition is to predict the overall relevance of an item for a given user, based on her/his own implicit feedback instead of using similar users ratings (commonly used in collaborative filtering). Furthermore, since we believe one criterion may be more important than others, we propose a weighting schema, in which we estimate how interesting is each criterion for a given user, in order to generate a personalized ranking. The weighting schema do not suppose the generation of predicted explicit ratings. Instead, we reorganize the weights in such a way that just the criterion that has rating are weighted. For predicting the weight of each criterion to each user, we propose a genetic programming to predict how interesting is each criterion for a user, in which the initial weight values are randomly generated. In our experiments, we show that when having a sufficient corpus of historical user implicit feedback we can obtain higher precision for ranking items to a user, considering a predicted set of weight.