OpenRaiser/Intern-Atlas huggingface dataset:
RAGUEL: Recourse-Aware Group Unfairness Elimination
Authors: Aparajita Haldar, Teddy Cunningham, Hakan Ferhatosmanoglu
Venue: Conference CIKM 2022
Year: 2022

While machine learning and ranking-based systems are in widespread use for sensitive decision-making processes (e.g., determining job candidates, assigning credit scores), they are rife with concerns over unintended biases in their outcomes, which makes algorithmic fairness (e.g., demographic parity, equal opportunity) an objective of interest. 'Algorithmic recourse' offers feasible recovery actions to change unwanted outcomes through the modification of attributes. We introduce the notion of ranked group-level recourse fairness, and develop a 'recourse-aware ranking' solution that satisfies ranked recourse fairness constraints while minimizing the cost of suggested modifications. Our solution suggests interventions that can reorder the ranked list of database records and mitigate group-level unfairness; specifically, disproportionate representation of sub-groups and recourse cost imbalance. This re-ranking identifies the minimum modifications to data points, with these attribute modifications weighted according to their ease of recourse. We then present an efficient block-based extension that enables re-ranking at any granularity (e.g., multiple brackets of bank loan interest rates, multiple pages of search engine results). Evaluation on real datasets shows that, while existing methods may even exacerbate recourse unfairness, our solution -- RAGUEL -- significantly improves recourse-aware fairness. RAGUEL outperforms alternatives at improving recourse fairness, through a combined process of counterfactual generation and re-ranking, whilst remaining efficient for large-scale datasets.
Social recommendation with implicit social influence
Authors: Changhao Song, Bo Wang 0011, Qinxue Jiang, Yehua Zhang, Ruifang He, Yuexian Hou
Venue: Conference SIGIR 2021
Year: 2021

Social influence is essential to social recommendation. Current influence-based social recommendation focuses on the explicit influence on observed social links. However, in real cases, implicit social influence can also impact users' preference in an unobserved way. In this work, we concern two kinds of implicit influence: Local Implicit Influence of persons on unobserved interpersonal relations, and Global Implicit Influence of items broadcasted to users. We improve the state-of-the-art GNN-based social recommendation methods by modeling two kinds of implicit influences separately. Local implicit influence is involved by predicting unobserved social relationships. Global implicit influence is involved by defining global popularity of each item and personalize the impact of the popularity on each user. In a GCN network, explicit and implicit influence are integrated to learn the social embedding of users and items in social recommendation. Experimental results on Yelp initially demonstrate the effectiveness of proposed model.
Accelerating serverless computing by harvesting idle resources
Authors: Hanfei Yu, Hao Wang, Jian Li, Xu Yuan, Seung-Jong Park
Venue: Conference WWW 2022
Year: 2022

Serverless computing automates fine-grained resource scaling and simplifies the development and deployment of online services with stateless functions. However, it is still non-trivial for users to allocate appropriate resources due to various function types, dependencies, and input sizes. Misconfiguration of resource allocations leaves functions either under-provisioned or over-provisioned and leads to continuous low resource utilization. This paper presents Freyr, a new resource manager (RM) for serverless platforms that maximizes resource efficiency by dynamically harvesting idle resources from over-provisioned functions to under-provisioned functions. Freyr monitors each function’s resource utilization in real-time, detects over-provisioning and under-provisioning, and learns to harvest idle resources safely and accelerates functions efficiently by applying deep reinforcement learning algorithms along with a safeguard mechanism. We have implemented and deployed a Freyr prototype in a 13-node Apache OpenWhisk cluster. Experimental results show that 38.8% of function invocations have idle resources harvested by Freyr, and 39.2% of invocations are accelerated by the harvested resources. Freyr reduces the 99th-percentile function response latency by 32.1% compared to the baseline RMs.
NDlib: a python library to model and analyze diffusion processes over complex networks
Authors: Giulio Rossetti, Letizia Milli, Salvatore Rinzivillo
Venue: Conference WWW 2018
Year: 2018

Nowadays the analysis of dynamics of and on networks represents a hot topic in the Social Network Analysis playground. To support students, teachers, developers and researchers we introduced a novel framework, named NDlib, an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. Upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians.
Task-adaptive few-shot node classification
Authors: Song Wang 0013, Kaize Ding, Chuxu Zhang, Chen Chen 0022, Jundong Li
Venue: Conference KDD 2022
Year: 2022

Node classification is of great importance among various graph mining tasks. In practice, real-world graphs generally follow the long-tail distribution, where a large number of classes only consist of limited labeled nodes. Although Graph Neural Networks (GNNs) have achieved significant improvements in node classification, their performance decreases substantially in such a few-shot scenario. The main reason can be attributed to the vast generalization gap between meta-training and meta-test due to the task variance caused by different node/class distributions in meta-tasks (i.e., node-level and class-level variance). Therefore, to effectively alleviate the impact of task variance, we propose a task-adaptive node classification framework under the few-shot learning setting. Specifically, we first accumulate meta-knowledge across classes with abundant labeled nodes. Then we transfer such knowledge to the classes with limited labeled nodes via our proposed task-adaptive modules. In particular, to accommodate the different node/class distributions among meta-tasks, we propose three essential modules to perform node-level, class-level, and task-level adaptations in each meta-task, respectively. In this way, our framework can conduct adaptations to different meta-tasks and thus advance the model generalization performance on meta-test tasks. Extensive experiments on four prevalent node classification datasets demonstrate the superiority of our framework over the state-of-the-art baselines. Our code is provided at https://github.com/SongW-SW/TENT https://github.com/SongW-SW/TENT.
Effects of search success on search engine re-use
Authors: Victor Hu, Maria Stone, Jan O. Pedersen, Ryen W. White
Venue: Conference CIKM 2011
Year: 2011

People's experiences when interacting with online services affects their decisions on reuse. Users of Web search engines are primarily focused on obtaining relevant information pertaining to their query. Search engines that fail to satisfy users' information needs may find their market share to be negatively affected. However, despite its importance to search providers, the relationship be-tween search success and search engine reuse is poorly understood. In this paper, we present a longitudinal log-based study with a large cohort of search engine users that quantifies the relationship between success and re-use of search engines. We use time series analysis to define two groups of users: stationary and non-stationary. We find that recent changes in satisfaction rate do correlate moderately with changes in rate of return for stationary users. For non-stationary users, we find that satisfaction and rate of return change together and in the same direction. We also find that some effects are stronger for a smaller player on the market than for a clear market leader, but both are affected. This is the first study to explore these issues in the context of Web search, and our findings have implications for search providers seeking to better understand their users and improving their experience.
Understanding temporal query dynamics
Authors: Anagha Kulkarni, Jaime Teevan, Krysta M. Svore, Susan T. Dumais
Venue: Conference WSDM 2011
Year: 2011

Web search is strongly influenced by time. The queries people issue change over time, with some queries occasionally spiking in popularity (e.g., earthquake) and others remaining relatively constant (e.g., youtube). The documents indexed by the search engine also change, with some documents always being about a particular query (e.g., the Wikipedia page on earthquakes is about the query earthquake) and others being about the query only at a particular point in time (e.g., the New York Times is only about earthquakes following a major seismic activity). The relationship between documents and queries can also change as people's intent changes (e.g., people sought different content for the query earthquake before the Haitian earthquake than they did after). In this paper, we explore how queries, their associated documents, and the query intent change over the course of 10 weeks by analyzing query log data, a daily Web crawl, and periodic human relevance judgments. We identify several interesting features by which changes to query popularity can be classified, and show that presence of these features, when accompanied by changes in result content, can be a good indicator of change in query intent.
Reducing Hubness: A Cause of Vulnerability in Recommender Systems
Authors: Kazuo Hara, Ikumi Suzuki, Kei Kobayashi, Kenji Fukumizu
Venue: Conference SIGIR 2015
Year: 2015

It is known that memory-based collaborative filtering systems are vulnerable to shilling attacks. In this paper, we demonstrate that hubness, which occurs in high dimensional data, is exploited by the attacks. Hence we explore methods for reducing hubness in user-response data to make these systems robust against attacks. Using the MovieLens dataset, we empirically show that the two methods for reducing hubness by transforming a similarity matrix(i) centering and (ii) conversion to a commute time kernel-can thwart attacks without degrading the recommendation performance.
MetaTPOT: enhancing a tree-based pipeline optimization tool using meta-learning
Authors: Doron Laadan, Roman Vainshtein, Yarden Curiel, Gilad Katz, Lior Rokach
Venue: Conference CIKM 2020
Year: 2020

Automatic machine learning (AutoML) aims to automate the different aspects of the data science process and, by extension, allow non-experts to utilize "off the shelf" machine learning solution. One of the more popular AutoML methods is the Tree-based Pipeline Optimization Tool (TPOT), which uses genetic programming (GP) to efficiently explore the vast space of ML pipelines and produce a working ML solution. However, TPOT's GP process comes with substantial time and computational costs. In this study, we explore TPOT's GP process and propose MetaTPOT, an enhanced variant that uses a meta learning-based approach to predict the performance of TPOT's pipeline candidates. MetaTPOT leverages domain knowledge in the form of pipelines pre-ranking to improve TPOT's speed and performance. Evaluation on 65 classification datasets shows that our approach often improves the outcome of the genetic process while simultaneously substantially reduce its running time and computational cost.
Optimal hashing schemes for entity matching
Authors: Nilesh N. Dalvi, Vibhor Rastogi, Anirban Dasgupta, Anish Das Sarma, Tamás Sarlós
Venue: Conference WWW 2013
Year: 2013

In this paper, we consider the problem of devising blocking schemes for entity matching. There is a lot of work on blocking techniques for supporting various kinds of predicates, e.g. exact matches, fuzzy string-similarity matches, and spatial matches. However, given a complex entity matching function in the form of a Boolean expression over several such predicates, we show that it is an important and non-trivial problem to combine the individual blocking techniques into an efficient blocking scheme for the entity matching function, a problem that has not been studied previously.
MealRec+: A Meal Recommendation Dataset with Meal-Course Affiliation for Personalization and Healthiness
Authors: Ming Li, Lin Li, Xiaohui Tao, Jimmy Xiangji Huang
Venue: Conference SIGIR 2024
Year: 2024

Meal recommendation, as a typical health-related recommendation task, contains complex relationships between users, courses, and meals. Among them, meal-course affiliation associates user-meal and user-course interactions. However, an extensive literature review demonstrates that there is a lack of publicly available meal recommendation datasets including meal-course affiliation. Meal recommendation research has been constrained in exploring the impact of cooperation between two levels of interaction on personalization and healthiness. To pave the way for meal recommendation research, we introduce a new benchmark dataset called MealRec$^+$. Due to constraints related to user health privacy and meal scenario characteristics, the collection of data that includes both meal-course affiliation and two levels of interactions is impeded. Therefore, a simulation method is adopted to derive meal-course affiliation and user-meal interaction from the user's dining sessions simulated based on user-course interaction data. Then, two well-known nutritional standards are used to calculate the healthiness scores of meals. Moreover, we experiment with several baseline models, including separate and cooperative interaction learning methods. Our experiment demonstrates that cooperating the two levels of interaction in appropriate ways is beneficial for meal recommendations. Furthermore, in response to the less healthy recommendation phenomenon found in the experiment, we explore methods to enhance the healthiness of meal recommendations. The dataset is available on GitHub (https://github.com/WUT-IDEA/MealRecPlus).
Strategy-aware bundle recommender system
Authors: Yinwei Wei, Xiaohao Liu, Yunshan Ma, Xiang Wang, Liqiang Nie, Tat-Seng Chua
Venue: Conference SIGIR 2023
Year: 2023

A bundle is a group of items that provides improved services to users and increased profits for sellers. However, locating the desired bundles that match the users' tastes still challenges us, due to the sparsity issue. Despite the remarkable performance of existing approaches, we argue that they seldom consider the bundling strategy (i.e., how the items within a bundle are associated with each other) in the bundle recommendation, resulting in the suboptimal user and bundle representations for their interaction prediction. Therefore, we propose to model the strategy-aware user and bundle representations for the bundle recommendation. Towards this end, we develop a new model for bundle recommendation, termed Bundle Graph Transformer (BundleGT), which consists of the token embedding layer, hierarchical graph transformer (HGT) layer, and prediction layer. Specifically, in the token embedding layer, we take the items within bundles as tokens and represent them with items' id embedding learned from user-item interactions. Having the input tokens, the HGT layer can simultaneously model the strategy-aware bundle and user representations. Therein, we encode the prior knowledge of bundling strategy from the well-designed bundles and incorporate it with tokens' embeddings to model the bundling strategy and learn the strategy-aware bundle representations. Meanwhile, upon the correlation between bundles consumed by the same user, we further learn the user preference on bundling strategy. Jointly considering it with the user preference on the item content, we can learn the strategy-aware user representation for user-bundle interaction prediction. Conducting extensive experiments on Youshu, ifashion, and Netease datasets, we demonstrate that our proposed model outperforms the state-of-the-art baselines (e.g., BundelNet [7] Net, BGCN [3] BGCN, and CrossCBR [22]), justifying the effectiveness of our proposed model. Moreover, in HGT layer, our devised light self-attention block improves not only the accuracy performance but efficiency of BundleGT. Our code is publicly available at: https://github.com/Xiaohao-Liu/BundleGT.
BiSR: Bidirectionally Optimized Super-Resolution for Mobile Video Streaming
Authors: Qian Yu, Qing Li, Rui He, Gareth Tyson, Wanxin Shi, Jianhui Lv, Zhenhui Yuan, Peng Zhang, Yulong Lan, Zhicheng Li
Venue: Conference WWW 2023
Year: 2023

The user experience of mobile web video streaming is often impacted by insufficient and dynamic network bandwidth. In this paper, we design Bidirectionally Optimized Super-Resolution (BiSR) to improve the quality of experience (QoE) for mobile web users under limited bandwidth. BiSR exploits a deep neural network (DNN)-based model to super-resolve key frames efficiently without changing the inter-frame spatial-temporal information. We then propose a downscaling DNN and a mobile-specific optimized lightweight super-resolution DNN to enhance the performance. Finally, a novel reinforcement learning-based adaptive bitrate (ABR) algorithm is proposed to verify the performance of BiSR on real network traces. Our evaluation, using a full system implementation, shows that BiSR saves 26% of bitrate compared to the traditional H.264 codec and improves the SSIM of video by 3.7% compared to the prior state-of-the-art. Overall, BiSR enhances the user-perceived quality of experience by up to 30.6%.
RotDiff: A hyperbolic rotation representation model for information diffusion prediction
Authors: Hongliang Qiao, Shanshan Feng, Xutao Li, Huiwei Lin, Han Hu, Wei Wei, Yunming Ye
Venue: Conference CIKM 2023
Year: 2023

The massive amounts of online user behavior data on social networks allow for the investigation of information diffusion prediction, which is essential to comprehend how information propagates among users. The main difficulty in diffusion prediction problem is to effectively model the complex social factors in social networks and diffusion cascades. However, existing methods are mainly based on Euclidean space, which cannot well preserve the underlying hierarchical structures that could better reflect the strength of user influence. Meanwhile, existing methods cannot accurately model the obvious asymmetric features of the diffusion process. To alleviate these limitations, we utilize rotation transformation in the hyperbolic to model complex diffusion patterns. The modulus of representations in the hyperbolic space could effectively describe the strength of the user's influence. Rotation transformations could represent a variety of complex asymmetric features. Further, rotation transformation could model various social factors without changing the strength of influence. In this paper, we propose a novel hyperbolic rotation representation model RotDiff for the diffusion prediction problem. Specifically, we first map each social user to a Lorentzian vector and use two groups of transformations to encode global social factors in the social graph and the diffusion graph. Then, we combine attention mechanism in the hyperbolic space with extra rotation transformations to capture local diffusion dependencies within a given cascade. Experimental results on five real-world datasets demonstrate that the proposed model RotDiff outperforms various state-of-the-art diffusion prediction models.
When Comparing to Ground Truth is Wrong: On Evaluating GNN Explanation Methods
Authors: Lukas Faber, Amin K. Moghaddam, Roger Wattenhofer
Venue: Conference KDD 2021
Year: 2021

We study the evaluation of graph explanation methods. The state of the art to evaluate explanation methods is to first train a GNN, then generate explanations, and finally compare those explanations with the ground truth. We show five pitfalls that sabotage this pipeline because the GNN does not use the ground-truth edges. Thus, the explanation method cannot detect the ground truth. We propose three novel benchmarks: (i) pattern detection, (ii) community detection, and (iii) handling negative evidence and gradient saturation. In a re-evaluation of state-of-the-art explanation methods, we show paths for improving existing methods and highlight further paths for GNN explanation research.
Conformal counterfactual inference under hidden confounding
Authors: Zonghao Chen, Ruocheng Guo, Jean-Francois Ton, Yang Liu 0018
Venue: Conference KDD 2024
Year: 2024

Personalized decision making requires the knowledge of potential outcomes under different treatments, and confidence intervals about the potential outcomes further enrich this decision-making process and improve its reliability in high-stakes scenarios. Predicting potential outcomes along with its uncertainty in a counterfactual world poses the foundamental challenge in causal inference. Existing methods that construct confidence intervals for counterfactuals either rely on the assumption of strong ignorability that completely ignores hidden confounders, or need access to un-identifiable lower and upper bounds that characterize the difference between observational and interventional distributions. In this paper, to overcome these limitations, we first propose a novel approach wTCP-DR based on transductive weighted conformal prediction, which provides confidence intervals for counterfactual outcomes with marginal converage guarantees, even under hidden confounding. With less restrictive assumptions, our approach requires access to a fraction of interventional data (from randomized controlled trials) to account for the covariate shift from observational distributoin to interventional distribution. Theoretical results explicitly demonstrate the conditions under which our algorithm is strictly advantageous to the naive method that only uses interventional data. Since transductive conformal prediction is notoriously costly, we propose wSCP-DR, a two-stage variant of wTCP-DR, based on split conformal prediction with same marginal coverage guarantees but at a significantly lower computational cost. After ensuring valid intervals on counterfactuals, it is straightforward to construct intervals for individual treatment effects (ITEs). We demonstrate our method across synthetic and real-world data, including recommendation systems, to verify the superiority of our methods compared against state-of-the-art baselines in terms of both coverage and efficiency. Our code can be found at https://github.com/rguo12/KDD24-Conformal.
Challenges toward AGI and its impact to the Web
Authors: Bo Zhang, Jie Tang
Venue: Conference WWW 2024
Year: 2024

Large language models have substantially advanced the state of the art in various AI tasks, such as natural language understanding and text generation, and image processing, and multimodal modeling. In this talk, we will first introduce the development of AI in the past decades, in particular from the angle of China. We will also talk about the opportunities, challenges, and risks of AGI in the future, and its impact on the Web. In the second part of the talk, we will use ChatGLM, an alternative but open sourced model to ChatGPT, as an example to explain our understandings and insights derived during the implementation of the model.