Print Email Facebook Twitter Item-Item Collaborative Filtering via Graph Regularization Title Item-Item Collaborative Filtering via Graph Regularization Author Koper ook geschreven Jansen, Melle (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Isufi, E. (mentor) Yang, M. (mentor) Zarras, A. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2022-06-22 Abstract A recommendation algorithm aims to predict the quality of a user's future interaction with certain items based on their previous interactions. As research progresses, these algorithms are becoming increasingly more complicated with the use of machine learning and neural networks. This paper looks into a more simple solution. The recommendation domain can be represented as a graph, meaning different graph regularization techniques can be used to solve the same problem. After running experiments comparing the Item-Item Tikhonov Regularizer and the Sobolev Regularizer to a baseline, the item-item standard Collaborative Filtering method, it is clear that the Graph Regularization techniques outperform the baseline. Given that it has been shown that Collaborative Filtering is a relatively competitive method in this field, outperforming it means that Graph Regularizers are a viable and potentially competitive method for solving the recommender problem. Subject Recommender SystemsGraph RegularizationTikhonov Regularizer To reference this document use: http://resolver.tudelft.nl/uuid:acfe69f3-1e21-4620-b727-2e3de816d766 Part of collection Student theses Document type bachelor thesis Rights © 2022 Melle Koper ook geschreven Jansen Files PDF Research_Paper_6_1.pdf 642.52 KB Close viewer /islandora/object/uuid:acfe69f3-1e21-4620-b727-2e3de816d766/datastream/OBJ/view