How Expressive are Graph Neural Networks in Recommendation?

Graph Neural Networks (GNNs) have demonstrated superior performance on various graph learning tasks, including recommendation, where they leverage user-item collaborative filtering signals in graphs. However, theoretical formulations of their capability are scarce, despite their empirical effectiveness in state-of-the-art recommender models. Recently, research has explored the expressiveness of GNNs in general, demonstrating that message passing GNNs are at most as powerful as the Weisfeiler-Lehman test, and that GNNs combined with random node initialization are universal. Nevertheless, the concept of "expressiveness" for GNNs remains vaguely defined. Most existing works adopt the graph isomorphism test as the metric of expressiveness, but this graph-level task may not effectively assess a model's ability in recommendation, where the objective is to distinguish nodes of different closeness. In this paper, we provide a comprehensive theoretical analysis of the expressiveness of GNNs in recommendation, considering three levels of expressiveness metrics: graph isomorphism (graph-level), node automorphism (node-level), and topological closeness (link-level). We propose the topological closeness metric to evaluate GNNs' ability to capture the structural distance between nodes, which aligns closely with the objective of recommendation. To validate the effectiveness of this new metric in evaluating recommendation performance, we introduce a learning-less GNN algorithm that is optimal on the new metric and can be optimal on the node-level metric with suitable modification. We conduct extensive experiments comparing the proposed algorithm against various types of state-of-the-art GNN models to explore the explainability of the new metric in the recommendation task. For reproducibility, implementation codes are available at https://github.com/HKUDS/GTE.Comment: 32nd ACM International Conference on Information and Knowledge Management (CIKM) 202

THE BATTLE FOR SINGLES’ DAY: HOW SOCIAL MEDIA MARKETING CAMPAIGNS BOOST SALES

Numerous studies have shown that social media marketing strategies have positive impacts on the long-term financial performance of firms. However, whether short-term marketing campaigns have any influence on firm revenue remains unknown. This paper examines data from Singles’ Day, the world’s largest shopping event, revealing that firms’ social media efforts have a positive impact on product sales. Furthermore, we find that the two social media effort measures generally thought to have positive impacts on a firm’s long-term financial performance, richness and intensity, have no significant influence on the success of a firm’s short-term marketing campaign. Instead, relevance shows significant and positive impacts. Moreover, we compare the effects of social media marketing yields from company-owned accounts with those of employee-owned accounts, finding that employee-owned accounts have better marketing effects than company-owned ones

Graph Transformer for Recommendation

This paper presents a novel approach to representation learning in recommender systems by integrating generative self-supervised learning with graph transformer architecture. We highlight the importance of high-quality data augmentation with relevant self-supervised pretext tasks for improving performance. Towards this end, we propose a new approach that automates the self-supervision augmentation process through a rationale-aware generative SSL that distills informative user-item interaction patterns. The proposed recommender with Graph TransFormer (GFormer) that offers parameterized collaborative rationale discovery for selective augmentation while preserving global-aware user-item relationships. In GFormer, we allow the rationale-aware SSL to inspire graph collaborative filtering with task-adaptive invariant rationalization in graph transformer. The experimental results reveal that our GFormer has the capability to consistently improve the performance over baselines on different datasets. Several in-depth experiments further investigate the invariant rationale-aware augmentation from various aspects. The source code for this work is publicly available at: https://github.com/HKUDS/GFormer.Comment: Accepted by SIGIR'202

SSLRec: A Self-Supervised Learning Framework for Recommendation

Self-supervised learning (SSL) has gained significant interest in recent years as a solution to address the challenges posed by sparse and noisy data in recommender systems. Despite the growing number of SSL algorithms designed to provide state-of-the-art performance in various recommendation scenarios (e.g., graph collaborative filtering, sequential recommendation, social recommendation, KG-enhanced recommendation), there is still a lack of unified frameworks that integrate recommendation algorithms across different domains. Such a framework could serve as the cornerstone for self-supervised recommendation algorithms, unifying the validation of existing methods and driving the design of new ones. To address this gap, we introduce SSLRec, a novel benchmark platform that provides a standardized, flexible, and comprehensive framework for evaluating various SSL-enhanced recommenders. The SSLRec framework features a modular architecture that allows users to easily evaluate state-of-the-art models and a complete set of data augmentation and self-supervised toolkits to help create SSL recommendation models with specific needs. Furthermore, SSLRec simplifies the process of training and evaluating different recommendation models with consistent and fair settings. Our SSLRec platform covers a comprehensive set of state-of-the-art SSL-enhanced recommendation models across different scenarios, enabling researchers to evaluate these cutting-edge models and drive further innovation in the field. Our implemented SSLRec framework is available at the source code repository https://github.com/HKUDS/SSLRec.Comment: Published as a WSDM'24 full paper (oral presentation

A Sentiment-based Hybrid Model for Stock Return Forecasting

Real-world financial time series often contain both linear and nonlinear patterns. However, traditional time series analysis models, such as ARIMA, hold the assumption that a linear correlation exists among time series values while leaving nonlinear relation into error terms. Based on financial theories, we argue that investor sentiment is the main contributor to nonlinear pattern of stock time series. Furthermore, we propose a sentiment-based hybrid model (SLNM) to better capture nonlinear information in stock time series. According to the forecasting experimental results, SLNM exhibits the sensitivity to sentiment environments, which in turn supports the argument that investor sentiment is the main source of nonlinear pattern in stock time series. For those stocks that are in top 10 of CAR Ranking List ─ these stocks are more likely pursed by emotional investors and thus in optimistic sentiment environment, SLNM improves forecasting performance dramatically: Increase Direction Accuracy by 40% and reduce RMSE by 19.3%. While, for those that are in bottom 10 of CAR Ranking List─ these stocks defer more emotional investors from further participating in stock trading and thus in pessimistic sentiment environment, SLNM has a fair improvement on performance: Hold the similar Direction Accuracy and reduce RMSE only by 2.5%. All these indicate that investor sentiment play a key role in stock return forecasting. Our work sheds light on the research of sentiment-based prediction models

Highly sampled measurements in a controlled atmosphere at the Biosphere 2 Landscape Evolution Observatory

Land-atmosphere interactions at different temporal and spatial scales are important for our understanding of the Earth system and its modeling. The Landscape Evolution Observatory (LEO) at Biosphere 2, managed by the University of Arizona, hosts three nearly identical artificial bare-soil hillslopes with dimensions of 11x30 m(2) (1m depth) in a controlled and highly monitored environment within three large greenhouses. These facilities provide a unique opportunity to explore these interactions. The dataset presented here is a subset of the measurements in each LEO's hillslopes, from 1 July 2015 to 30 June 2019 every 15minutes, consisting of temperature, water content and heat flux of the soil (at 5cm depth) for 12 co-located points; temperature, relative humidity and wind speed above ground at 5 locations and 5 different heights ranging from 0.25m to 9-10m; 3D wind at 1 location; the four components of radiation at 2 locations; spatially aggregated precipitation rates, total subsurface discharge, and relative water storage; and the measurements from a weather station outside the greenhouses.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Highly Sampled Measurements in a Controlled Atmosphere at the Biosphere 2 Landscape Evolution Observatory

Land-atmosphere interactions at different temporal and spatial scales are important for our understanding of the Earth system and its modeling. The Landscape Evolution Observatory (LEO) at Biosphere 2 managed by the University of Arizona host three nearly identical artificial bare-soil hillslopes with dimensions of 30m x 11m and 1m average depth in a controlled and highly monitored environment under a large greenhouse. These facilities provide a unique opportunity to explore these interactions. This dataset contains, for each one of the three replicate hillslopes, 15-minute measurements from July 1, 2015 to June 30, 2019 of temperature, water content and heat flux of the soil at a depth of 5cm for 12 co-located points; temperature, relative humidity and wind speed above ground at 5 different locations over each hillslope and 5 different heights ranging from 0.25m to 9-10m; 3D wind components at 1 location; the 4 components of radiation at 2 different locations; precipitation rates; and the measurements of an automatic weather station outside the greenhouse