Explaining Session-based Recommendations using Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{lipinski:2024:GECCOcomp,

• author = "Piotr Lipinski and Klaudia Balcer",
• title = "Explaining Session-based Recommendations using Grammatical Evolution",
• booktitle = "Evolutionary Computation and Explainable AI",
• year = "2024",
• editor = "John McCall and Jaume Bacardit and Giovanni Iacca",
• pages = "1590--1597",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, explainable artificial intelligence, XAI, evolutionary algorithms, recommender systems, session-based recommender systems, latent vector representations",
• isbn13 = "979-8-4007-0495-6",
• DOI = "doi:10.1145/3638530.3664156",
• size = "8 pages",
• abstract = "This paper concerns explaining session-based recommendations using Grammatical Evolution. A session-based recommender system processes a given sequence of products browsed by a user and suggests the most relevant next product to display to the user. State-of-the-art session-based recommender systems are often a type of deep learning black box, so explaining their results is a challenge.In this paper, we propose an approach with a grammatical expression that provides explanations of recommendations generated by session-based recommender systems as well as an evolutionary algorithm, GE-XAI-SBRS, based on Grammatical Evolution, with its own initialization and crossover operators, to construct such a grammatical expression. Our approach uses latent product representations, so-called vector embeddings, generated by the recommender systems and providing some additional knowledge on dependencies between products.Computational experiments on the YooChoose dataset being one of the most popular session-based benchmarks, and the recommendations generated by the Target Attentive Graph Neural Network (TAGNN) model confirm the usefulness of the proposed approach, the efficiency of the proposed algorithm and outperforming the regular GE algorithm in the task under consideration.",
• notes = "GECCO-2024 ECXAI A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Piotr Lipinski Klaudia Balcer

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