Recommender Systems: Learning Collaborative Filtering Similarity Measures Using Siamese Networks

Author
Keywords
Abstract
Improving 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.
Year of Publication
In Press
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
In press
Start Page
1
Issue
In press
Number
In press
Number of Pages
1-7
Date Published
03/2025
ISSN Number
1989-1660
URL
DOI
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