01777nas a2200241   4500000000100000000000100001008004100002260001200043653002800055653002000083653002100103653002300124653002100147653002300168100002100191700002300212245010100235856005800336300000800394490001300402520110600415022001401521       9998                            d  c03/202510aCollaborative Filtering10aNeural Networks10aOne-Hot Encoding10aRecommender System10aSiamese Networks10aSimilarity Measure1 aJesús Bobadilla1 aAbraham Gutiérrez00aRecommender Systems: Learning Collaborative Filtering Similarity Measures Using Siamese Networks  uhttps://www.ijimai.org/journal/bibcite/reference/3573  a1-70 vIn press3 aImproving current similarity measures in the collaborative filtering Recommender Systems is relevant, since it contributes to different applications such as to get better big data representations of users and items, to implement dynamic browsers able to navigate through data, and to explain recommendation results. Currently, there are
many statistically based similarity measures, some of them tailored to the extraordinarily sparse collaborative filtering scenario. Nevertheless, the hypothesis of the paper is that using neural networks, learnt similarity measures can be obtained that improve existing ones. To accomplish the task, the typical neural models cannot be used, and it is necessary to focus on the similarity learning area, in which the goal is to make the modellearn, which is a similarity function able to measure how similar two objects are. Siamese networks adequately implement the similarity learning concept, and we have adapted them to collaborative filtering particularities. The results in different scenarios show significant improvements compared to the state-of-the-art.  a1989-1660