Applying landmarks to enhance memory-based collaborative filtering

Memory-based Collaborative Filtering (CF) has been a widely used approach for personalised recommendation with considerable success in many applications. An important issue regarding memory-based CF lies in similarity computation: the sparsity of the rating matrix leads to similarity computations ba...

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Bibliographic Details
Published in:Information sciences Vol. 513; pp. 412 - 428
Main Authors: Lima, Gustavo R., Mello, Carlos E., Lyra, Adria, Zimbrao, Geraldo
Format: Journal Article
Language:English
Published: Elsevier Inc 01.03.2020
Subjects:
Collaborative filtering
Dimensionality reduction
Landmarks
Memory-based algorithms
Non-linear transformations
Recommender system
Dimensionality reduction
Landmarks
Memory-based algorithms
Collaborative filtering
Non-linear transformations
Recommender system
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:Memory-based Collaborative Filtering (CF) has been a widely used approach for personalised recommendation with considerable success in many applications. An important issue regarding memory-based CF lies in similarity computation: the sparsity of the rating matrix leads to similarity computations based on few co-rated items between users, resulting in high sensitive predictions. Additionally, the ‘sparse’ similarity computation has high computational cost, due to the dimensionality of the item space. In this paper, we pursue both these issues. We propose a new model to compute similarity by representing users (or items) through their distances to preselected users, named landmarks. Such user modelling allows the introduction of more ratings into similarity computations through transitive relations created by the landmarks. Unlike conventional memory-based CF, the proposal builds a new user space defined by distances to landmarks, avoiding sensitivity in similarity computations. Findings from our experiments show that the proposed modelling achieves better accuracy than the ‘sparse’ similarity representation in all tested datasets, and has also yielded competitive accuracy results against the compared model-based CF algorithms. Furthermore, the proposed implementation has beaten all compared methods in terms of computational performance, becoming a promising alternative to memory-based CF algorithms for large datasets.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2019.10.041