Existing multi-behavior recommendations tend to prioritize performance at the expense of explainability, while current explainable methods suffer from limited generalizability due to their reliance on external information. Neuro-Symbolic integration offers a promising avenue for explainability by combining neural networks with symbolic logic rule reasoning. Concurrently, we posit that user behavior chains (e.g., view->cart->buy) inherently embody an endogenous logic suitable for explicit reasoning. However, these observational multiple behaviors are plagued by confounders, causing models to learn spurious correlations. By incorporating causal inference into this Neuro-Symbolic framework, we propose a novel Causal Neuro-Symbolic Reasoning model for Explainable Multi-Behavior Recommendation (CNRE). CNRE operationalizes the endogenous logic by simulating a human-like decision-making process. Specifically, CNRE first employs hierarchical preference propagation to capture heterogeneous cross-behavior dependencies. Subsequently, it models the endogenous logic rule implicit in the user's behavior chain based on preference strength, and adaptively dispatches to the corresponding neural-logic reasoning path (e.g., conjunction, disjunction). This process generates an explainable causal mediator that approximates an ideal state isolated from confounding effects. Extensive experiments on three large-scale datasets demonstrate CNRE's significant superiority over state-of-the-art baselines, offering multi-level explainability from model design and decision process to recommendation results.

This study contributes to our understanding of how the emergence of the COVID-19 pandemic changes the global Banking Financial Services and Insurance (BFSI) landscape. Before the COVID-19 pandemic, BFSIs corporate strategy was solely aligned to the quest for operational efficiency. However, during the ongoing COVID-19 pandemic, global BFSIs are forced to adopt digital transformation in their operations due to a rise in transaction volumes. The ongoing COVID-19 pandemic already triggers holistic innovations concerning the global BFSI's product, process, concept, trend, or idea. Thus, the BFSI cannot survive without efficient and innovative system software for global operations. The study plots the hype cycle to identify relevant technologies to deal with real-world business problems. The hype cycle indicates that the need for advanced data integration is growing and COVID-19 pandemic has already triggered it. The study argues that the incorporation of data integration might be challenging initially for BFSIs but eventually it may result in an efficient model to handle these types of pandemic or unexpected circumstances.