@inproceedings{125ad945a070433bac1a06a7dce366f3,
title = "PD-SRS: personalized diversity for a fair session-based recommendation system",
abstract = "Session-based Recommender Systems (SRSs), which aim to recommend users{\textquoteright} next action based on their current and historical sessions, play a significant role in many real-world online services. The existing session-based recommendation methods have mainly focused on the accuracy of recommendation, which biases to reinforce popular items/services and loses the recommendation diversity. Diversity is a positive aspect particularly in SRSs as the target user may like to be surprised and interact with a broader range of content in different sessions. In this work, we propose a Personalized Diversification strategy for a Session-based Recommender System (PD-SRS) using graph neural networks. Comprehensive experiments are carried out on two real-world datasets to demonstrate the effectiveness of PD-SRS in making a trade-off between accuracy and personalized diversity over the baselines.",
keywords = "Session-based recommendation, Personalized diversity, Fairness, Long-tail recommendation, Graph neural network",
author = "{Ranjbar Kermany}, Naime and Luiz Pizzato and Jian Yang and Shan Xue and Jia Wu",
year = "2022",
doi = "10.1007/978-3-031-20984-0_23",
language = "English",
isbn = "9783031209833",
series = "Lecture Notes in Computer Science",
publisher = "Springer, Springer Nature",
pages = "331--339",
editor = "Javier Troya and Brahim Medjahed and Mario Piattini and Lina Yao and Pablo Fern{\'a}ndez and Antonio Ruiz-Cort{\'e}s",
booktitle = "Service-oriented computing",
address = "United States",
note = "20th International Conference on Service-Oriented Computing, ICSOC 2022 ; Conference date: 29-11-2022 Through 02-12-2022",
}