The proliferation of Large Language Models (LLMs) has established LLM routing as a standard service delivery mechanism, where users select models based on cost, Quality of Service (QoS), among other things. However, optimal pricing in LLM routing platforms requires precise modeling for dynamic service markets, and solving this problem in real time at scale is computationally intractable. In this paper, we propose PriLLM, a novel practical and scalable solution for real-time dynamic pricing in competitive LLM routing. PriLLM models the service market as a Stackelberg game, where providers set prices and users select services based on multiple criteria. To capture real-world market dynamics, we incorporate both objective factors (eg cost, QoS) and subjective user preferences into the model. For scalability, we employ a deep aggregation network to learn provider abstraction that preserve user-side equilibrium behavior across pricing strategies. Moreover, PriLLM offers interpretability by explaining its pricing decisions. Empirical evaluation on real-world data shows that PriLLM achieves over 95% of the optimal profit while only requiring less than 5% of the optimal solution's computation time.

Predicting traffic speed is a crucial task in intelligent transportation systems, as it helps analyze traffic congestion and improve road flow. The complex spatio-temporal interactions present in traffic data make accurate predictions challenging. In recent years, many studies have focused on extracting and learning spatio-temporal features. Deep learning methods, particularly spatio-temporal graph learning models, show promising performance and become the mainstream approach in this area of research. However, existing methods cannot exploit and unify the heterogeneous spatio-temporal interactions hidden in traffic data to achieve multi-correlation modeling. As a result, they cannot effectively model the complex evolving patterns in traffic dynamics. To this end, we propose a heterogeneous spatio-temporal traffic graph learning framework (HSTGL) to capture these diverse spatio-temporal interactions comprehensively. In terms of design, HSTGL consists of three modules: spatio-temporal heterogeneous graph construction, spatio-temporal heterogeneous graph attention learning, and heterogeneous information supplementation. The first two modules utilize similar temporal pattern clustering and heterogeneous spatio-temporal graph attention mechanisms (HSTGAT) to learn heterogeneous spatio-temporal interactions in traffic data. The latter feature fusion module (FFM) is developed to complement potential heterogeneous information in the global spatio-temporal context. Our HSTGL conducts extensive experiments on three real-world public traffic datasets: METR-LA, PEMS-BAY, and PEMSD7M. The results demonstrate that HSTGL achieves superior predictive performance compared to representative benchmark methods with average improvements of 3.3% in MAE, 1.8% in RMSE, and 5.2% in MAPE compared to the optimal baseline.

This paper studies cost-effective graph cleaning with a single machine. We adopt a rule-based method that may embed machine learning models as predicates in the rules. Graph cleaning with the rules involves rule discovery, error detection and correction. These tasks are both computation-heavy and I/O-intensive as they repeatedly invoke costly graph pattern matching, and produce a large amount of a large volume of intermediate results, among other things. In light of these, no existing single-machine system is able to carry out these tasks even on not-too-large graphs, even using GPUs. Thus we develop MiniClean, a single-machine system for cleaning large graphs. It proposes (1) a workflow that better fits a single machine by pipelining CPU, GPU and I/O operations; (2) memory footprint reduction with bundled processing and data compression; and (3) a multi-mode parallel model for SIMD, pipelined and independent parallelism, and their scheduling to maximize CPU--GPU synergy. Using real-life graphs, we empirically verify that MiniClean outperforms the SOTA single-machine systems by at least 65.34× and multi-machine systems with 32 nodes by at least 8.09×.

The task of point-of-interest (POI) recommendation aims to recommend locations to users in location-based applications. Among them, the task of time-aware POI recommendation aims to capture the user’s preferences that change dynamically over time, so as to make more accurate recommendations to users at a specific time. While existing works take into account the spatial, temporal and category context of POIs, they cannot capture user preferences that are more fine-grained than the category granularity. Additionally, RNN-based methods suffer from the problem of long-term dependency when capturing a user’s check-in patterns. To address these challenges, we propose a novel model with POI multi-grained grouping method which captures the user’s co-visit patterns and weekly patterns, to obtain finer-grained POI groups. The model also utilizes the transformer model to capture the user’s check-in preference patterns. We evaluate our model on two real-world datasets, and the experimental results demonstrate the effectiveness of our proposed model.