Dynamic pricing, which suggests the optimal prices based on the dynamic demands, has received considerable attention in academia and industry. On online hotel booking platforms, room demand fluctuates due to various factors, notably hotel popularity and competition. In this paper, we propose a dynamic pricing approach with popularity and competitiveness-aware demand learning. Specifically, we introduce a novel demand function that incorporates popularity and competitiveness coefficients to comprehensively model the price elasticity of demand. We develop a dynamic demand prediction network that focuses on learning these coefficients in the proposed demand function, enhancing the interpretability and accuracy of price suggestion. The model is trained in a multi-task framework that effectively leverages the correlations of demands among groups of similar hotels to alleviate data sparseness in room-level occupancy prediction. Comprehensive experiments conducted on real-world datasets validate the superiority of our method over state-of-the-art baselines in both demand prediction and dynamic pricing. Our model has been successfully deployed on a popular online travel platform, serving tens of millions of users and hoteliers.

Community search, which looks for query-dependent communities in a graph, is an important task in graph analysis. Existing community search studies address the problem by finding a densely-connected subgraph containing the query. However, many real-world networks are heterogeneous with rich semantics. Queries in heterogeneous networks generally involve in multiple communities with different semantic connections, while returning a single community with mixed semantics has limited applications. In this paper, we revisit the community search problem on heterogeneous networks and introduce a novel paradigm of heterogeneous community search and ranking. We propose to automatically discover the query semantics to enable the search of different semantic communities and develop a comprehensive community evaluation model to support the ranking of results. We build HeteroCS, a heterogeneous community search system with semantic explanation, upon our semantic community model, and deploy it on two real-world graphs. We present a demonstration case to illustrate the novelty and effectiveness of the system.