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Sequential recommendation involves suggesting subsequent items in a series of user activities. When recommending relevant items to users within the same account, the challenge lies in discerning diverse user behaviours to provide tailored recommendations based on individual preferences and timing. Cross‐domain sequential recommendation (CDSR) focuses on accurately extracting cross‐domain user preferences from both within‐sequence and between‐sequence interactions among items. Current approaches typically concentrate on learning preferences within a single domain using intra‐sequence item interactions, followed by a transfer module for cross‐domain preferences. However, this sequential process and implicit method are constrained by the effectiveness of the transfer module and may overlook inter‐sequence item associations. In this study, we propose an optimal cluster with attention‐based shared‐account cross‐domain sequential recommendation (O‐SCSR) system using deep reinforcement learning techniques. Our approach commences by formulating a modified hummingbird optimization (MHO) algorithm for clustering, effectively identifying latent users who share the same account to enhance the understanding of user interactions within shared‐account scenarios. Additionally, we design a domain filter based on quantum classic deep reinforcement learning (QCDRL), intelligently selecting interactions contributing to O‐SCSR. By quantifying rewards from transferred domain knowledge, the QCDRL‐based filter retains only valuable interactions for the task of SCDR. Finally, we validate the efficacy of our proposed O‐SCDR method using real‐world datasets, namely HVIDEO and HAMAZON. Through simulation results comparing the O‐SCDR system with existing state‐of‐the‐art systems, we demonstrate its effectiveness and legitimacy. [ABSTRACT FROM AUTHOR]

Recommender system (RS) aims to predict user preferences based on automatic data acquisition, and those collected data assist in achieving the final decision. However, RS suffers from data sparsity issues over the newly launched system, and the lack of time to deal with the massive data is also a challenging factor. To acquire proper outcomes, cross-domain RS intends to transfer knowledge from the specific domain with quality enriched data to help recommendations to the target domains. The entities may or may not be overlapped, and it is common for the entities of two domains to be overlapped. These overlapping entities may show variations in their target domain, and avoiding these issues leads to distorted prediction outcomes over the cross-domain RS. To address these issues, this research concentrates on modeling and efficient cross-domain RS using the generative and discriminative adversarial network (CRS-GDAN) model for kernel-based transfer modeling. Domain specific is considered to handle the feature space of overlapped entities, and transfer computation is adopted to handle the overlapping and non-overlapping entity correlation among the domains. Based on the anticipated concept, knowledge transfer is achieved rigorously even in the case of overlapping entities, thus diminishing the data sparsity issues. The experimentation is performed using an available online dataset, and the model attains a 20% better outcome than other approaches. The outcomes specify that the knowledge transfer from source to destination target is advantageous even in overlapping issues. [ABSTRACT FROM AUTHOR]