  This paper investigates Cross-Domain Sequential Recommendation (CDSR), a
promising method that uses information from multiple domains (more than three)
to generate accurate and diverse recommendations, and takes into account the
sequential nature of user interactions. The effectiveness of these systems
often depends on the complex interplay among the multiple domains. In this
dynamic landscape, the problem of negative transfer arises, where heterogeneous
knowledge between dissimilar domains leads to performance degradation due to
differences in user preferences across these domains. As a remedy, we propose a
new CDSR framework that addresses the problem of negative transfer by assessing
the extent of negative transfer from one domain to another and adaptively
assigning low weight values to the corresponding prediction losses. To this
end, the amount of negative transfer is estimated by measuring the marginal
contribution of each domain to model performance based on a cooperative game
theory. In addition, a hierarchical contrastive learning approach that
incorporates information from the sequence of coarse-level categories into that
of fine-level categories (e.g., item level) when implementing contrastive
learning was developed to mitigate negative transfer. Despite the potentially
low relevance between domains at the fine-level, there may be higher relevance
at the category level due to its generalised and broader preferences. We show
that our model is superior to prior works in terms of model performance on two
real-world datasets across ten different domains.

Artificial Intelligence;Machine Learning