The problem of data sparsity has long been a challenge in recommendation systems, and previous studies have attempted to address this issue by incorporating side information. However, this approach often introduces side effects such as noise, availability issues, and low data quality, which in turn hinder the accurate modeling of user preferences and adversely impact recommendation performance. In light of the recent advancements in large language models (LLMs), which possess extensive knowledge bases and strong reasoning capabilities, we propose a novel framework called LLMRec that enhances recommender systems by employing three simple yet effective LLM-based graph augmentation strategies. Our approach leverages the rich content available within online platforms (e.g., Netflix, MovieLens) to augment the interaction graph in three ways: (i) reinforcing user-item interaction egde, (ii) enhancing the understanding of item node attributes, and (iii) conducting user node profiling, intuitively from the natural language perspective. By employing these strategies, we address the challenges posed by sparse implicit feedback and low-quality side information in recommenders. Besides, to ensure the quality of the augmentation, we develop a denoised data robustification mechanism that includes techniques of noisy implicit feedback pruning and MAE-based feature enhancement that help refine the augmented data and improve its reliability. Furthermore, we provide theoretical analysis to support the effectiveness of LLMRec and clarify the benefits of our method in facilitating model optimization. Experimental results on benchmark datasets demonstrate the superiority of our LLM-based augmentation approach over state-of-the-art techniques. To ensure reproducibility, we have made our code and augmented data publicly available at: https://github.com/HKUDS/LLMRec.git

pdf/llmrec-large-language-models-with-graph-augmentation-for-134zlw87l8.pdf

LLMRec: Large Language Models with Graph Augmentation for Recommendation

. Lumbini, in southern Nepal, is a UNESCO world heritage site of universal value as the birthplace of Buddha. Poor air quality in Lumbini and surrounding regions is a great concern for public health as well as for preservation, protection and promotion of Buddhist heritage and culture. We present here results from measurements of ambient concentrations of key air pollutants (PM, BC, CO, O3) in Lumbini, first of its kind for Lumbini, conducted during an intensive measurement period of 3 months (April–June 2013) in the pre-monsoon season. The measurements were carried out as a part of the international air pollution measurement campaign; SusKat-ABC (Sustainable Atmosphere for the Kathmandu Valley – Atmospheric Brown Clouds). The main objective of this work is to understand and document the level of air pollution, diurnal characteristics and influence of open burning on air quality in Lumbini. The hourly average concentrations during the entire measurement campaign ranged as follows: BC was 0.3–30.0 µg m−3, PM1 was 3.6–197.6 µg m−3, PM2. 5 was 6.1–272.2 µg m−3, PM10 was 10.5–604.0 µg m−3, O3 was 1.0–118.1 ppbv and CO was 125.0–1430.0 ppbv. These levels are comparable to other very heavily polluted sites in South Asia. Higher fraction of coarse-mode PM was found as compared to other nearby sites in the Indo-Gangetic Plain region. The ΔBC ∕ ΔCO ratio obtained in Lumbini indicated considerable contributions of emissions from both residential and transportation sectors. The 24 h average PM2. 5 and PM10 concentrations exceeded the WHO guideline very frequently (94 and 85 % of the sampled period, respectively), which implies significant health risks for the residents and visitors in the region. These air pollutants exhibited clear diurnal cycles with high values in the morning and evening. During the study period, the worst air pollution episodes were mainly due to agro-residue burning and regional forest fires combined with meteorological conditions conducive of pollution transport to Lumbini. Fossil fuel combustion also contributed significantly, accounting for more than half of the ambient BC concentration according to aerosol spectral light absorption coefficients obtained in Lumbini. WRF-STEM, a regional chemical transport model, was used to simulate the meteorology and the concentrations of pollutants to understand the pollutant transport pathways. The model estimated values were ∼ 1. 5 to 5 times lower than the observed concentrations for CO and PM10, respectively. Model-simulated regionally tagged CO tracers showed that the majority of CO came from the upwind region of Ganges Valley. Model performance needs significant improvement in simulating aerosols in the region. Given the high air pollution level, there is a clear and urgent need for setting up a network of long-term air quality monitoring stations in the greater Lumbini region.

/pdf/pre-monsoon-air-quality-over-lumbini-a-world-heritage-site-1b1a2vqm9k.pdf

Pre-monsoon air quality over Lumbini, a world heritage site along the Himalayan foothills

In this paper, we introduce a new self-supervised rationalization method, called KGRec, for knowledge-aware recommender systems. To effectively identify informative knowledge connections, we propose an attentive knowledge rationalization mechanism that generates rational scores for knowledge triplets. With these scores, KGRec integrates generative and contrastive self-supervised tasks for recommendation through rational masking. To highlight rationales in the knowledge graph, we design a novel generative task in the form of masking-reconstructing. By masking important knowledge with high rational scores, KGRec is trained to rebuild and highlight useful knowledge connections that serve as rationales. To further rationalize the effect of collaborative interactions on knowledge graph learning, we introduce a contrastive learning task that aligns signals from knowledge and user-item interaction views. To ensure noise-resistant contrasting, potential noisy edges in both graphs judged by the rational scores are masked. Extensive experiments on three real-world datasets demonstrate that KGRec outperforms state-of-the-art methods. We also provide the implementation codes for our approach at https://github.com/HKUDS/KGRec.

/pdf/knowledge-graph-self-supervised-rationalization-for-2r7b8x15.pdf

Knowledge Graph Self-Supervised Rationalization for Recommendation

Ice Giant Systems: The scientific potential of orbital missions to Uranus and Neptune

Mercury's magnetopause is unique in the solar system due to its relatively small size and its close proximity to the Sun. Based on 3 years of MErcury Surface, Space ENvironment, GEochemistry, and Ranging orbital Magnetometer and the Fast Imaging Plasma Spectrometer data, the mean magnetopause location was determined for a total of 5694 passes. We fit these magnetopause locations to a three-dimensional nonaxially symmetric magnetopause which includes an indentation for the cusp region that has been successfully applied to the Earth. Our model predicts that Mercury's magnetopause is highly indented surrounding the cusp with central depth ~0.64 RM and large dayside extension. The dayside polar magnetopause dimension is, thus, smaller than the equatorial magnetopause dimension. Cross sections of the dayside magnetopause in planes perpendicular to the Mercury-Sun line are prolate and elongated along the dawn-dusk direction. In contrast, the magnetopause downstream of the terminator plane is larger in the north-south than the east-west directions by a ratio of 2.6 RM to 2.2 RM at a distance of 1.5 RM downstream of Mercury. Due to the northward offset of the internal dipole, the model predicts that solar wind has direct access to the surface of Mercury at middle magnetic latitudes in the southern hemisphere. During extremely high solar wind pressure conditions, the northern hemisphere middle magnetic latitudes may also be subject to direct solar wind impact.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2015JA021425

Mercury's three-dimensional asymmetric magnetopause

Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, graph neural networks (GNNs) based model has gradually become the theme of knowledge-aware recommendation (KGR). However, there is a natural deficiency for GNN-based KGR models, that is, the sparse supervised signal problem, which may make their actual performance drop to some extent. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring the contrastive learning in KG-aware recommendation and propose a novel multi-level cross-view contrastive learning mechanism, named MCCLK. Different from traditional contrastive learning methods which generate two graph views by uniform data augmentation schemes such as corruption or dropping, we comprehensively consider three different graph views for KG-aware recommendation, including global-level structural view, local-level collaborative and semantic views. Specifically, we consider the user-item graph as a collaborative view, the item-entity graph as a semantic view, and the user-item-entity graph as a structural view. MCCLK hence performs contrastive learning across three views on both local and global levels, mining comprehensive graph feature and structure information in a self-supervised manner. Besides, in semantic view, a k-Nearest-Neighbor (k NN) item-item semantic graph construction module is proposed, to capture the important item-item semantic relation which is usually ignored by previous work. Extensive experiments conducted on three benchmark datasets show the superior performance of our proposed method over the state-of-the-arts. The implementations are available at: https://github.com/CCIIPLab/MCCLK.

Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System

Network alignment (NA) is the task of finding the correspondence of nodes between two networks. Since most existing NA methods have attempted to discover every node pair at once, they may fail to utilize node pairs that have strong consistency across different networks in the NA task. To tackle this challenge, we propose Grad-Align, a new NA method that gradually discovers node pairs by making full use of either node pairs exhibiting strong consistency or prior matching information. Specifically, the proposed method gradually aligns nodes based on both the similarity of embeddings generated using graph neural networks (GNNs) and the Tversky similarity, which is an asymmetric set similarity using the Tversky index applicable to networks with different scales. Experimental evaluation demonstrates that Grad-Align consistently outperforms state-of-the-art NA methods in terms of the alignment accuracy. Our source code is available at https://github.com/jindeok/Grad-Align.

/pdf/grad-align-gradual-network-alignment-via-graph-neural-3md80ypy.pdf

Grad-Align: Gradual Network Alignment via Graph Neural Networks (Student Abstract)

Road networks are widely used as a fundamental structure in urban transportation studies. In recent years, with more research leveraging deep learning to solve conventional transportation problems, how to obtain robust road network representations (i.e., embeddings) applicable for a wide range of applications became a fundamental need. Existing studies mainly adopt graph embedding methods. Such methods, however, foremost learn the topological correlations of road networks but ignore the spatial structure (i.e., spatial correlations) which are also important in applications such as querying similar trajectories. Besides, most studies learn task-specic embeddings in a supervised manner such that the embeddings are sub-optimal when being used for new tasks. It is inecient to store or learn dedicated embeddings for every dierent task in a large transportation system. To tackle these issues, we propose a model named SARN to learn generic and task-agnostic road network embeddings based on self-supervised contrastive learning. We present (i) a spatial similarity matrix to help learn the spatial correlations of the roads, (ii) a sampling strategy based on the spatial structure of a road network to form self-supervised training samples, and (iii) a two-level loss function to guide SARN to learn embeddings based on both local and global contrasts of similar and dissimilar road segments. Experimental results on three downstream tasks over real-world road networks show that SARN outperforms state-of-the-art self-supervised models consistently and achieves comparable (or even better) performance to supervised models.

Spatial Structure-Aware Road Network Embedding via Graph Contrastive Learning

Asymmetric Structure of Uranus' Magnetopause Controlled by IMF and Planetary Rotation

A Seasonal Study of Uranus' Magnetosphere

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