OpenRaiser/Intern-Atlas huggingface dataset:
GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection
Authors: Shijie Zhang, Hongzhi Yin, Tong Chen 0005, Quoc Viet Hung Nguyen, Zi Huang, Lizhen Cui
Venue: Conference SIGIR 2020
Year: 2020

In recent years, recommender system has become an indispensable function in all e-commerce platforms. The review rating data for a recommender system typically comes from open platforms, which may attract a group of malicious users to deliberately insert fake feedback in an attempt to bias the recommender system to their favour. The presence of such attacks may violate modeling assumptions that high-quality data is always available and these data truly reflect users' interests and preferences. Therefore, it is of great practical significance to construct a robust recommender system that is able to generate stable recommendations even in the presence of shilling attacks. In this paper, we propose GraphRfi - a GCN-based user representation learning framework to perform robust recommendation and fraudster detection in a unified way. In its end-to-end learning process, the probability of a user being identified as a fraudster in the fraudster detection component automatically determines the contribution of this user's rating data in the recommendation component; while the prediction error outputted in the recommendation component acts as an important feature in the fraudster detection component. Thus, these two components can mutually enhance each other. Extensive experiments have been conducted and the experimental results show the superiority of our GraphRfi in the two tasks - robust rating prediction and fraudster detection. Furthermore, the proposed GraphRfi is validated to be more robust to the various types of shilling attacks over the state-of-the-art recommender systems.
Value penalized q-learning for recommender systems
Authors: Chengqian Gao, Ke Xu, Kuangqi Zhou, Lanqing Li, Xueqian Wang, Bo Yuan, Peilin Zhao
Venue: Conference SIGIR 2022
Year: 2022

Scaling reinforcement learning (RL) to recommender systems (RS) is promising since maximizing the expected cumulative rewards for RL agents meets the objective of RS, i.e., improving customers' long-term satisfaction. A key approach to this goal is offline RL, which aims to learn policies from logged data. However, the high-dimensional action space and the non-stationary dynamics in commercial RS intensify distributional shift issues, making it challenging to apply offline RL methods to RS. To alleviate the action distribution shift problem in extracting RL policy from static trajectories, we propose Value Penalized Q-learning (VPQ), an uncertainty-based offline RL algorithm. It penalizes the unstable Q-values in the regression target by uncertainty-aware weights, without the need to estimate the behavior policy, suitable for RS with a large number of items. We derive the penalty weights from the variances across an ensemble of Q-functions. To alleviate distributional shift issues at test time, we further introduce the critic framework to integrate the proposed method with classic RS models. Extensive experiments conducted on two real-world datasets show that the proposed method could serve as a gain plugin for existing RS models.
Bayesian inference for plackett-luce ranking models
Authors: John Guiver, Edward Lloyd Snelson
Venue: Conference ICML 2009
Year: 2009

This paper gives an efficient Bayesian method for inferring the parameters of a Plackett-Luce ranking model. Such models are parameterised distributions over rankings of a finite set of objects, and have typically been studied and applied within the psychometric, sociometric and econometric literature. The inference scheme is an application of Power EP (expectation propagation). The scheme is robust and can be readily applied to large scale data sets. The inference algorithm extends to variations of the basic Plackett-Luce model, including partial rankings. We show a number of advantages of the EP approach over the traditional maximum likelihood method. We apply the method to aggregate rankings of NASCAR racing drivers over the 2002 season, and also to rankings of movie genres.
On optimizing top-k metrics for neural ranking models
Authors: Rolf Jagerman, Zhen Qin, Xuanhui Wang, Michael Bendersky, Marc Najork
Venue: Conference SIGIR 2022
Year: 2022

Top-K metrics such as NDCG@K are frequently used to evaluate ranking performance. The traditional tree-based models such as LambdaMART, which are based on Gradient Boosted Decision Trees (GBDT), are designed to optimize NDCG@K using the LambdaRank losses. Recently, there is a good amount of research interest on neural ranking models for learning-to-rank tasks. These models are fundamentally different from the decision tree models and behave differently with respect to different loss functions. For example, the most popular ranking losses used in neural models are the Softmax loss and the GumbelApproxNDCG loss. These losses do not connect to top-K metrics such as NDCG@K naturally. It remains a question on how to effectively optimize NDCG@K for neural ranking models. In this paper, we follow the LambdaLoss framework and design novel and theoretically sound losses for NDCG@K metrics, while the original LambdaLoss paper can only do so using an unsound heuristic. We study the new losses on the LETOR benchmark datasets and show that the new losses work better than other losses for neural ranking models.
Domain adaptation for enterprise email search
Authors: Brandon Tran, Maryam Karimzadehgan, Rama Kumar Pasumarthi, Michael Bendersky, Donald Metzler
Venue: Conference SIGIR 2019
Year: 2019

In the enterprise email search setting, the same search engine often powers multiple enterprises from various industries: technology, education, manufacturing, etc. However, using the same global ranking model across different enterprises may result in suboptimal search quality, due to the corpora differences and distinct information needs. On the other hand, training an individual ranking model for each enterprise may be infeasible, especially for smaller institutions with limited data. To address this data challenge, in this paper we propose a domain adaptation approach that fine-tunes the global model to each individual enterprise. In particular, we propose a novel application of the Maximum Mean Discrepancy (MMD) approach to information retrieval, which attempts to bridge the gap between the global data distribution and the data distribution for a given individual enterprise. We conduct a comprehensive set of experiments on a large-scale email search engine, and demonstrate that the MMD approach consistently improves the search quality for multiple individual domains, both in comparison to the global ranking model, as well as several competitive domain adaptation baselines including adversarial learning methods.
Feature transformation for neural ranking models
Authors: Honglei Zhuang, Xuanhui Wang, Michael Bendersky, Marc Najork
Venue: Conference SIGIR 2020
Year: 2020

Although neural network models enjoy tremendous advantages in handling image and text data, tree-based models still remain competitive for learning-to-rank tasks with numerical data. A major strength of tree-based ranking models is the insensitivity to different feature scales, while neural ranking models may suffer from features with varying scales or skewed distributions. Feature transformation or normalization is a simple technique which preprocesses input features to mitigate their potential adverse impact on neural models. However, due to lack of studies, it is unclear to what extent feature transformation can benefit neural ranking models. In this paper, we aim to answer this question by providing empirical evidence for learning-to-rank tasks. First, we present a list of commonly used feature transformation techniques and perform a comparative study on multiple learning-to-rank data sets. Then we propose a mixture feature transformation mechanism which can automatically derive a mixture of basic feature transformation functions to achieve the optimal performance. Our experiments show that applying feature transformation can substantially improve the performance of neural ranking models compared to directly using the raw features. In addition, the proposed mixture transformation method can further improve the performance of the ranking model without any additional human effort.
TILDE: Term Independent Likelihood Model for Passage Re-ranking
Authors: Shengyao Zhuang, Guido Zuccon
Venue: Conference SIGIR 2021
Year: 2021

Deep language models (deep LMs) are increasingly being used for full text retrieval or within cascade retrieval pipelines as later-stage re-rankers. A problem with using deep LMs is that, at query time, a slow inference step needs to be performed -- this hinders the practical adoption of these powerful retrieval models, or limits sensibly how many documents can be considered for re-ranking.
Element-guided temporal graph representation learning for temporal sets prediction
Authors: Le Yu, Guanghui Wu, Leilei Sun, Bowen Du, Weifeng Lv
Venue: Conference WWW 2022
Year: 2022

Given a sequence of sets with timestamps, where each set includes an arbitrary number of elements, temporal sets prediction aims to predict elements in the consecutive set. Indeed, predicting temporal sets is much more complicated than the conventional predictions of time series and temporal events. Recent studies on temporal sets prediction follow the same pipeline that only learns from each user’s own sequence, which fails to discover the collaborative signals among the sequences of different users. In this paper, we propose a novel element-guided temporal graph neural network to tackle the above issue in temporal sets prediction. Specifically, we first connect sequences of different users via a temporal graph, where nodes contain users and elements, and edges represent user-element interactions with time information. Then, we devise a new message aggregation mechanism to improve the model expressive ability via adaptively learning element-specific representations for each user with the guidance of elements. By performing the element-guided message aggregation among multiple hops, collaborative signals latent in high-order user-element interactions are explicitly encoded. Finally, we present a temporal information utilization module to capture both the semantic and periodic patterns in user sequential behaviors. Experiments on real-world datasets demonstrate that our approach could not only outperform the existing methods with a significant margin but also capture the collaborative signals. Codes and datasets are available at https://github.com/yule-BUAA/ETGNN.
Efficient marginal likelihood optimization in blind deconvolution
Authors: Anat Levin, Yair Weiss, Frédo Durand, William T. Freeman
Venue: Conference CVPR 2011
Year: 2011

In blind deconvolution one aims to estimate from an input blurred image y a sharp image x and an unknown blur kernel k. Recent research shows that a key to success is to consider the overall shape of the posterior distribution p(x, k\y) and not only its mode. This leads to a distinction between MAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x, k</sub> strategies which estimate the mode pair x, k and often lead to undesired results, and MAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> strategies which select the best k while marginalizing over all possible x images. The MAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> principle is significantly more robust than the MAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x, k</sub> one, yet, it involves a challenging marginalization over latent images. As a result, MAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> techniques are considered complicated, and have not been widely exploited. This paper derives a simple approximated MAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> algorithm which involves only a modest modification of common MAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x, k</sub> algorithms. We show that MAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> can, in fact, be optimized easily, with no additional computational complexity.
κhgcn: Tree-likeness modeling via continuous and discrete curvature learning
Authors: Menglin Yang 0001, Min Zhou 0006, Lujia Pan, Irwin King
Venue: Conference KDD 2023
Year: 2023

The prevalence of tree-like structures, encompassing hierarchical structures and power law distributions, exists extensively in real-world applications, including recommendation systems, ecosystems, financial networks, social networks, etc. Recently, the exploitation of hyperbolic space for tree-likeness modeling has garnered considerable attention owing to its exponential growth volume. Compared to the flat Euclidean space, the curved hyperbolic space provides a more amenable and embeddable room, especially for datasets exhibiting implicit tree-like architectures. However, the intricate nature of real-world tree-like data presents a considerable challenge, as it frequently displays a heterogeneous composition of tree-like, flat, and circular regions. The direct embedding of such heterogeneous structures into a homogeneous embedding space (i.e., hyperbolic space) inevitably leads to heavy distortions. To mitigate the aforementioned shortage, this study endeavors to explore the curvature between discrete structure and continuous learning space, aiming at encoding the message conveyed by the network topology in the learning process, thereby improving tree-likeness modeling. To the end, a curvature-aware hyperbolic graph convolutional neural network, κHGCN, is proposed, which utilizes the curvature to guide message passing and improve long-range propagation. Extensive experiments on node classification and link prediction tasks verify the superiority of the proposal as it consistently outperforms various competitive models by a large margin.
Mining optimal decision trees from itemset lattices
Authors: Siegfried Nijssen, Élisa Fromont
Venue: Conference KDD 2007
Year: 2007

We present DL8, an exact algorithm for finding a decision tree that optimizes a ranking function under size, depth, accuracy and leaf constraints. Because the discovery of optimal trees has high theoretical complexity, until now few efforts have been made to compute such trees for real-world datasets. An exact algorithm is of both scientific and practical interest. From a scientific point of view, it can be used as a gold standard to evaluate the performance of heuristic constraint-based decision tree learners and to gain new insight in traditional decision tree learners. From the application point of view, it can be used to discover trees that cannot be found by heuristic decision tree learners. The key idea behind our algorithm is that there is a relation between constraints on decision trees and constraints on itemsets. We show that optimal decision trees can be extracted from lattices of itemsets in linear time. We give several strategies to efficiently build these lattices. Experiments show that under the same constraints, DL8 obtains better results than C4.5, which confirms that exhaustive search does not always imply overfitting. The results also show that DL8 is a useful and interesting tool to learn decision trees under constraints.
Guiding mathematical reasoning via mastering commonsense formula knowledge
Authors: Jiayu Liu 0001, Zhenya Huang, Zhiyuan Ma 0006, Qi Liu 0003, Enhong Chen, Tianhuang Su, Haifeng Liu 0004
Venue: Conference KDD 2023
Year: 2023

Math formulas (e.g., "distance = speed X time'') serve as one of the fundamental commonsense knowledge in human cognition, where humans naturally acquire and manipulate them in logical thinking for mathematical reasoning problems. However, existing reasoning models mainly focus on learning heuristic linguistics or patterns to generate answers, but do not pay enough attention on learning with such formula knowledge. Thus, they are not transparent (thus uninterpretable) in terms of understanding and grasping basic mathematical logic. In this paper, to promote a step forward in the domain, we first construct two datasets (Math23K-F and MAWPS-F) with precise annotations of formula usage in each reasoning step for math word problems. Especially, our datasets are refined on the benchmark datasets, and thus ensure the generality and comparability for relevant research. Then, we propose a novel Formula-mastered Solver (FOMAS) with the guidance of mastering formula knowledge to solve the problems. Specifically, we establish FOMAS with two systems drawing insight from the dual process theory, including a Knowledge System and a Reasoning System, to learn and apply formula knowledge, respectively. The Knowledge System accumulates the math formulas, where we propose a novel pretraining manner to mimic how humans grasp the mathematical logic behind them. Then, in the Reasoning System, we develop elaborate formula-guided symbol prediction and goal generation methods that retrieve the necessary formula knowledge from Knowledge System to improve both reasoning accuracy and interpretability. It organically simulates how humans conduct complex reasoning under the explicit instruction of math formulas. Experimental results prove that FOMAS has a stronger reasoning ability and achieves a more interpretable reasoning process, which verifies the necessity of introducing formula knowledge transparently.
Improving knowledge tracing with collaborative information
Authors: Ting Long, Jiarui Qin, Jian Shen, Weinan Zhang, Wei Xia, Ruiming Tang, Xiuqiang He, Yong Yu
Venue: Conference WSDM 2022
Year: 2022

Knowledge tracing, which estimates students' knowledge states by predicting the probability that they correctly answer questions, is an essential task for online learning platforms. It has gained much attention in the decades due to its importance to downstream tasks like learning material arrangement, etc. The previous deep learning-based methods trace students' knowledge states with the explicitly intra-student information, i.e., they only consider the historical information of individuals to make predictions. However, they neglect the inter-student information, which contains the response correctness of other students who have similar question-answering experiences, may offer some valuable clues. Based on this consideration, we propose a method called Collaborative Knowledge Tracing (CoKT) in this paper, which sufficiently exploits the inter-student information in knowledge tracing. It retrieves the sequences of peer students who have similar question-answering experiences to obtain the inter-student information, and integrates the inter-student information with the intra-student information to trace students' knowledge states and predict their correctness in answering questions. We validate the effectiveness of our method on four real-world datasets and compare it with 11 baselines. The experimental results reveal that CoKT achieves the best performance.
Modeling relational drug-target-disease interactions via tensor factorization with multiple web sources
Authors: Huiyuan Chen, Jing Li
Venue: Conference WWW 2019
Year: 2019

Modeling the behaviors of drug-target-disease interactions is crucial in the early stage of drug discovery and holds great promise for precision medicine and personalized treatments. The growing availability of new types of data on the internet brings great opportunity of learning a more comprehensive relationship among drugs, targets, and diseases. However, existing methods often consider drug-target interactions or drug-disease interactions separately, which ignores the dependencies among these three entities. Also, many of them cannot directly incorporate rich heterogeneous information from diverse sources. In this work, we investigate the utility of tensor factorization to model the relationships of drug-target-disease, specifically leveraging different types of online data. Our motivation is two-fold. First, in human metabolic systems, many drugs interact with protein targets in cells to modulate target activities, which in turn alter biological pathways to promote healthy functions and to treat diseases. Instead of binary relationships of or , a tighter triple relationships should be exploited to better understand drug mechanism of actions (MoAs). Second, medical data could be collected from different sources (i.e., drug's chemical structure, target's sequence, or expression measurements). Therefore, effectively exploiting the complementarity among multiple sources is of great importance. Our method elegantly explores a tensor together with complementarity among different data sources, thus improves prediction accuracy. We achieve this goal by formulating the problem into a coupled tensor-matrix factorization problem and directly optimize it on the nonlinear manifold. Experimental results on real-world datasets show that the proposed model outperforms several competitive methods. Our model opens up opportunities to use large Web data to predict drugs' MoAs in pharmacological studies.
Taxonomy-aware multi-hop reasoning networks for sequential recommendation
Authors: Jin Huang, Zhaochun Ren, Wayne Xin Zhao, Gaole He, Ji-Rong Wen, Daxiang Dong
Venue: Conference WSDM 2019
Year: 2019

In this paper, we focus on the task of sequential recommendation using taxonomy data. Existing sequential recommendation methods usually adopt a single vectorized representation for learning the overall sequential characteristics, and have a limited modeling capacity in capturing multi-grained sequential characteristics over context information. Besides, existing methods often directly take the feature vectors derived from context information as auxiliary input, which is difficult to fully exploit the structural patterns in context information for learning preference representations. To address above issues, we propose a novel Taxonomy-aware Multi-hop Reasoning Network, named TMRN, which integrates a basic GRU-based sequential recommender with an elaborately designed memory-based multi-hop reasoning architecture. For enhancing the reasoning capacity, we incorporate taxonomy data as structural knowledge to instruct the learning of our model. We associate the learning of user preference in sequential recommendation with the category hierarchy in the taxonomy. Given a user, for each recommendation, we learn a unique preference representation corresponding to each level in the taxonomy based on her/his overall sequential preference. In this way, the overall, coarse-grained preference representation can be gradually refined in different levels from general to specific, and we are able to capture the evolvement and refinement of user preference over the taxonomy, which makes our model highly explainable. Extensive experiments show that our proposed model is superior to state-of-the-art baselines in terms of both effectiveness and interpretability.