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View all- Zhang DZhu YDong YWang YFeng WKharlamov ETang J(2023)ApeGNN: Node-Wise Adaptive Aggregation in GNNs for RecommendationProceedings of the ACM Web Conference 202310.1145/3543507.3583530(759-769)Online publication date: 30-Apr-2023
Density-Ratio Based Personalised Ranking from Implicit Feedback
Pages 3221 - 3233
Abstract
Learning from implicit user feedback is challenging as we can only observe positive samples but never access negative ones. Most conventional methods cope with this issue by adopting a pairwise ranking approach with negative sampling. However, the pairwise ranking approach has a severe disadvantage in the convergence time owing to the quadratically increasing computational cost with respect to the sample size; it is problematic, particularly for large-scale datasets and complex models such as neural networks. By contrast, a pointwise approach does not directly solve a ranking problem, and is therefore inferior to a pairwise counterpart in top-K ranking tasks; however, it is generally advantageous in regards to the convergence time. This study aims to establish an approach to learn personalised ranking from implicit feedback, which reconciles the training efficiency of the pointwise approach and ranking effectiveness of the pairwise counterpart. The key idea is to estimate the ranking of items in a pointwise manner; we first reformulate the conventional pointwise approach based on density ratio estimation and then incorporate the essence of ranking-oriented approaches (e.g. the pairwise approach) into our formulation. Through experiments on three real-world datasets, we demonstrate that our approach dramatically reduces the convergence time (one to two orders of magnitude faster) and significantly improves the ranking performance.
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April 2021
4054 pages
ISBN:9781450383127
DOI:10.1145/3442381
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Published: 03 June 2021
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View all- Zhang DZhu YDong YWang YFeng WKharlamov ETang J(2023)ApeGNN: Node-Wise Adaptive Aggregation in GNNs for RecommendationProceedings of the ACM Web Conference 202310.1145/3543507.3583530(759-769)Online publication date: 30-Apr-2023
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