Graph neural network-based recommenders employ the aggregation paradigms to learn node representation from higher-order neighboring nodes within the graph. However, these simple aggregation paradigms may perform poorly when mitigating noise impacts and capturing complex user preferences. To address it, some studies have attempted to enhance representation through contrastive augmentation across different views. Despite some effectiveness, the simple-view contrasts are still suboptimal with some unresolved challenges: (1) the influence of multivariate noise in interaction data, (2) knowledge biases introduced by irrelevant connections, and (3) user’s multiple interests. In this work, we propose a novel method named Diffusion-Based Relation Augmentation for Knowledge-aware Recommendation (DRAR) to overcome the above challenges. First, we alleviate the impact of interaction noise by injecting uncertainty and generating preference distributions with a diffusion-based module. Next, we design a relation augmentation module to effectively capture user neighborhood-level and context-level enhanced representations to alleviate the knowledge bias of irrelevant connections. Furthermore, we design a collaborative alignment module that enhances the model’s robustness by aligning user representation views at different stages. Extensive experiments on three benchmark datasets consistently demonstrate the superiority of our model over the state-of-the-art approaches. Our model demonstrates average improvements of 6.78% in Recall and 7.38% in NDCG across all datasets.

As a fundamental operation of location-based services, shortest path querying is widely adopted in real-time applications. Regrettably, most prior works overlook the impact of traffic-flow on shortest path querying. Taking traffic-flow into account is essential for finding a more convenient path through the Flow-Aware Shortest Path Querying (FSPQ). FSPQ faces the following challenges: (1) index restriction, existing indexes are only constructed by the relative spatial distance, if we leverage the traffic-flow to build the index, we can reduce the index size and improve its query efficiency. (2) maintenance latency, the traffic-flow and edges' weights undergo high-frequency changes with different traffic conditions, meaning that our index must be able to support high-frequency updates. To end this, we propose a novel Flow-Aware Hierarchical Labeling Index (FAHL) in this paper. In the index construction aspect, we propose a degree-flow joint ordering method to obtain the joint vertex ordering, and then build the index on it. In this way, FAHL can not only perceive both spatial distance and traffic-flow information but also reduce the index overhead during the query. In the index maintenance aspect, we propose Improved Structure Update (ISU) and Index Label Update (ILU) algorithms to support the index updating when high-frequency flow$\backslash$weight changes. Moreover, a flow priority shortest path search algorithm with pruning query bounds is proposed to speed up the query processing. Extensive experiments demonstrate that our proposed method achieves 33.1% speedup on average for the flow-aware shortest path querying compared to the state-of-the-art methods.

The mainstream approach of GNN-based recommendation aggregates high-order ID information associated with the node in the user-item graph. The aggregation pattern using ID as signal has two disadvantages: lack of textual semantics and the impact of interaction noise. These disadvantages pose a threat to effectively learn user preferences, especially in capturing intricate user-item semantic relationships. Although large language models (LLMs) allow the integration of rich textual information into recommenders and have had groundbreaking applications in recommender systems, current works need to bridge the gap between different representation spaces. This is because LLM-based methods align the representations of GNN-based models only by using text embedding of LLM, leading to unsatisfactory results. To address this challenge, we propose a denoising alignment framework with LLMs for GNN-based recommenders (DALR) , which aims to align structural representation with textual representation and mitigate the effects of noise. Specifically, we propose a modeling framework that integrates the representation of graph structure with textual information from LLMs to capture intricate user-item interactions. We also suggest an alignment paradigm to enhance representation performance by aligning semantic signals from LLMs and structural features from GNN models. Additionally, we introduce a contrastive learning scheme to relieve the impact of noise and improve model performance. Extensive experiments on public datasets demonstrate that our model consistently outperforms the state-of-the-art methods. DALR achieves improvements ranging from 2.82% to 12.20% in Recall@5 and from 1.04% to 3.48% in NDCG@5 compared to the strongest baseline model, using the Steam dataset as an example.

As a fundamental technology in intelligent transportation systems (ITS), accurate traffic flow prediction has emerged as a critical challenge in real-time applications. How to fully utilize the traffic data, and capture the spatial temporal correlation are keys to improve the model's prediction ability. Numerous neural networks have been proposed to address this issue. However, most of these existing methods have the following two problems: 1) Lack of byroads information. Meaning that the existing methods do not consider the byroads in real-life traffic environments; 2) Lack of potential learning ability. Meaning that the existing methods suffer the non-similar forgetting and hard to gain the multi-hop correlation. To overcome these problems, we propose a novel Spatial Temporal Byroad-Aware Graph Convolution Network (ByGCN) in this paper. ByGCN consists of byroad identification and spatial temporal learning modules. In the first module, we design spatial temporal decoupling and graph diffusion blocks to identify the byroads and reconstruct them into the flow data. In the second module, with the help of spatial temporal attention and GCN, our module can capture the complex spatial temporal correlation. Experiments on four real-world traffic datasets demonstrate that ByGCN outperforms the state-of-the-art methods.

A shortest distance query is a fundamental operation of various real-time response applications in time-dependent road networks. Unfortunately, existing methods (e.g., G-treelike, 2-hop labeling-like) are prohibitively expensive in terms of space/time. To this end, we propose a novel Double Hierarchical Labeling (DHL) index, which consists of a Hierarchical Graph Partition (HGP) tree and a hierarchical border labeling list. For HGP-tree, we first use a hierarchical graph partitioning to split the entire road network into hierarchical subgraphs and then index these subgraphs by a balanced tree. To preserve all connectivity information between border vertices of subgraphs, a Time-based Distance Inverted File (TDIF) is constructed for each leaf node of the HGP-tree. For the hierarchical labeling list, we construct it only for border vertices and use it to speed up query processing. Moreover, a label propagation update is proposed to manage label updating when weights change. Finally, we propose a phase-aware search algorithm for different search situations between given query vertices to guarantee query efficiency. Extensive experiments are conducted to demonstrate the superiority of the proposed proposals on query processing and index maintenance.