OpenRaiser/Intern-Atlas huggingface dataset:
Jointmap: joint query intent understanding for modeling intent hierarchies in e-commerce search
Authors: Ali Ahmadvand, Surya Kallumadi, Faizan Javed, Eugene Agichtein
Venue: Conference SIGIR 2020
Year: 2020

An accurate understanding of a user's query intent can help improve the performance of downstream tasks such as query scoping and ranking. In the e-commerce domain, recent work in query understanding focuses on the query to product-category mapping. But, a small yet significant percentage of queries (in our website 1.5% or 33M queries in 2019) have non-commercial intent associated with them. These intents are usually associated with non-commercial information seeking needs such as discounts, store hours, installation guides, etc. In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks: 1) identifying a query's commercial vs. non-commercial intent, and 2) associating a set of relevant product categories in taxonomy to a product query. JointMap model works by leveraging the transfer bias that exists between these two related tasks through a joint-learning process. As curating a labeled data set for these tasks can be expensive and time-consuming, we propose a distant supervision approach in conjunction with an active learning model to generate high-quality training data sets. To demonstrate the effectiveness of JointMap, we use search queries collected from a large commercial website. Our results show that JointMap significantly improves both "commercial vs. non-commercial" intent prediction and product category mapping by 2.3% and 10% on average over state-of-the-art deep learning methods. Our findings suggest a promising direction to model the intent hierarchies in an e-commerce search engine.
Certified Robustness to Word Substitution Ranking Attack for Neural Ranking Models
Authors: Chen Wu, Ruqing Zhang, Jiafeng Guo, Wei Chen, Yixing Fan, Maarten de Rijke, Xueqi Cheng
Venue: Conference CIKM 2022
Year: 2022

Neural ranking models (NRMs) have achieved promising results in information retrieval. NRMs have also been shown to be vulnerable to adversarial examples. A typical Word Substitution Ranking Attack (WSRA) against NRMs was proposed recently, in which an attacker promotes a target document in rankings by adding human-imperceptible perturbations to its text. This raises concerns when deploying NRMs in real-world applications. Therefore, it is important to develop techniques that defend against such attacks for NRMs. In empirical defenses adversarial examples are found during training and used to augment the training set. However, such methods offer no theoretical guarantee on the models' robustness and may eventually be broken by other sophisticated WSRAs. To escape this arms race, rigorous and provable certified defense methods for NRMs are needed. To this end, we first define the \textit{Certified Top-$K$ Robustness} for ranking models since users mainly care about the top ranked results in real-world scenarios. A ranking model is said to be Certified Top-$K$ Robust on a ranked list when it is guaranteed to keep documents that are out of the top $K$ away from the top $K$ under any attack. Then, we introduce a Certified Defense method, named CertDR, to achieve certified top-$K$ robustness against WSRA, based on the idea of randomized smoothing. Specifically, we first construct a smoothed ranker by applying random word substitutions on the documents, and then leverage the ranking property jointly with the statistical property of the ensemble to provably certify top-$K$ robustness. Extensive experiments on two representative web search datasets demonstrate that CertDR can significantly outperform state-of-the-art empirical defense methods for ranking models.
Cross-domain recommendation via preference propagation graphnet
Authors: Cheng Zhao, Chenliang Li, Cong Fu
Venue: Conference CIKM 2019
Year: 2019

Recommendation can be framed as a graph link prediction task naturally. The user-item interaction graph built within a single domain often suffers from high sparsity. Thus, there has been a surge of approaches to alleviate the sparsity issue via cross-domain mutual augmentation. The SOTA cross-domain recommendation algorithms all try to bridge the gap via knowledge transfer in the latent space. We find there are mainly three problems in their formulations: 1) their knowledge transfer is unaware of the cross-domain graph structure. 2) their framework cannot capture high-order information propagation on the graph. 3) their cross-domain transfer formulations are generally more complicated to be optimized than the unified methods. In this paper, we propose the Preference Propagation GraphNet (PPGN) to address the above problems. Specifically, we construct a Cross-Domain Preference Matrix (CDPM) to model the interactions of different domains as a whole. Through the propagation layer of PPGN, we try to capture how user preferences propagate in the graph. Consequently, a joint objective for different domains is defined, and we simplify the cross-domain recommendation into a unified multi-task model. Extensive experiments on two pairs of real-world datasets show PPGN outperforms the SOTA algorithms significantly.
CollabSum: exploiting multiple document clustering for collaborative single document summarizations
Authors: Xiaojun Wan, Jianwu Yang
Venue: Conference SIGIR 2007
Year: 2007

Almost all existing methods conduct the summarization tasks for single documents separately without interactions for each document under the assumption that the documents are considered independent of each other. This paper proposes a novel framework called CollabSum for collaborative single document summarizations by making use of mutual influences of multiple documents within a cluster context. In this study, CollabSum is implemented by first employing the clustering algorithm to obtain appropriate document clusters and then exploiting the graph-ranking based algorithm for collaborative document summarizations within each cluster. Both the with-document and cross-document relationships between sentences are incorporated in the algorithm. Experiments on the DUC2001 and DUC2002 datasets demonstrate the encouraging performance of the proposed approach. Different clustering algorithms have been investigated and we find that the summarization performance relies positively on the quality of document cluster.
Look Ahead: Improving the Accuracy of Time-Series Forecasting by Previewing Future Time Features
Authors: Seonmin Kim, Dong-Kyu Chae
Venue: Conference SIGIR 2023
Year: 2023

Time-series forecasting has been actively studied and adopted in various real-world domains. Recently there have been two research mainstreams in this area: building Transformer-based architectures such as Informer, Autoformer and Reformer, and developing time-series representation learning frameworks based on contrastive learning such as TS2Vec and CoST. Both efforts have greatly improved the performance of time series forecasting. In this paper, we investigate a novel direction towards improving the forecasting performance even more, which is orthogonal to the aforementioned mainstreams as a model-agnostic scheme. We focus on time stamp embeddings that has been less-focused in the literature. Our idea is simple-yet-effective: based on given current time stamp, we predict embeddings of its near future time stamp and utilize the predicted embeddings in the time-series (value) forecasting task. We believe that if such future time information can be previewed at the time of prediction, they can be utilized by any time-series forecasting models as useful additional information. Our experimental results confirmed that our method consistently and significantly improves the accuracy of the recent Transformer-based models and time-series representation learning frameworks. Our code is available at: https://github.com/sunsunmin/Look_Ahead
A comparison of statistical significance tests for information retrieval evaluation
Authors: Mark D. Smucker, James Allan, Ben Carterette
Venue: Conference CIKM 2007
Year: 2007

Information retrieval (IR) researchers commonly use three tests of statistical significance: the Student's paired t-test, the Wilcoxon signed rank test, and the sign test. Other researchers have previously proposed using both the bootstrap and Fisher's randomization (permutation) test as non-parametric significance tests for IR but these tests have seen little use. For each of these five tests, we took the ad-hoc retrieval runs submitted to TRECs 3 and 5-8, and for each pair of runs, we measured the statistical significance of the difference in their mean average precision. We discovered that there is little practical difference between the randomization, bootstrap, and t tests. Both the Wilcoxon and sign test have a poor ability to detect significance and have the potential to lead to false detections of significance. The Wilcoxon and sign tests are simplified variants of the randomization test and their use should be discontinued for measuring the significance of a difference between means.
Ambulatory atrial fibrillation monitoring using wearable photoplethysmography with deep learning
Authors: Yichen Shen, Maxime Voisin, Alireza Aliamiri, Anand Avati, Awni Y. Hannun, Andrew Y. Ng
Venue: Conference KDD 2019
Year: 2019

We develop an algorithm that accurately detects Atrial Fibrillation (AF) episodes from photoplethysmograms (PPG) recorded in ambulatory free-living conditions. We collect and annotate a dataset containing more than 4000 hours of PPG recorded from a wrist-worn device. Using a 50-layer convolutional neural network, we achieve a test AUC of 95% in presence of motion artifacts inherent to PPG signals. Such continuous and accurate detection of AF has the potential to transform consumer wearable devices into clinically useful medical monitoring tools.
Efficient mining of emerging patterns: Discovering trends and differences
Authors: Guozhu Dong, Jinyan Li
Venue: Conference KDD 1999
Year: 1999

We introduce a new kind of patterns, called emerging patterns (EPs), for knowledge discovery from databases. EPs are defined as itemsets whose supports increase significantly from one dataset to another. EPs can capture emerging trends in timestamped databases, or useful contrasts between data classes. EPs have been proven useful: we have used them to build very powerful classifiers, which are more accurate than C4.5 and CBA, for many datasets. We believe that EPs with low to medium support, such as 1%-20%, can give useful new insights and guidance to experts, in even “well understood” applications. The efficient mining of EPs is a challenging problem, since (i) the Apriori property no longer holds for EPs, and (ii) there are usually too many candidates for high dimensional databases or for small support thresholds such as 0.5%. Naive algorithms are too costly. To solve this problem, (a) we promote the description of large collections of itemsets using their concise borders (the pair of sets of the minimal and of the maximal itemsets in the collections). (b) We design EP mining algorithms which manipulate only borders of collections (especially using our multiborder- differential algorithm), and which represent discovered EPs using borders. All EPs satisfying a constraint can be efficiently discovered by our border-based algorithms, which take the borders, derived by Max-Miner, of large itemsets as inputs. In our experiments on large and high dimensional datasets including the US census and Mushroom datasets, many EPs, including some with large cardinality, are found quickly. We also give other algorithms for discovering general or special types of EPs.
Discovering Succinct Pattern Sets Expressing Co-Occurrence and Mutual Exclusivity
Authors: Jonas Fischer, Jilles Vreeken
Venue: Conference KDD 2020
Year: 2020

Pattern mining is one of the core topics of data mining. We consider the problem of mining a succinct set of patterns that together explain the data in terms of mutual exclusivity and co-occurence. That is, we extend the traditional pattern languages beyond conjunctions, enabling us to capture more complex relationships, such as replacable sub-components or antagonists in biological pathways.
Fast and memory-efficient significant pattern mining via permutation testing
Authors: Felipe Llinares-López, Mahito Sugiyama, Laetitia Papaxanthos, Karsten M. Borgwardt
Venue: Conference KDD 2015
Year: 2015

We present a novel algorithm for significant pattern mining, Westfall-Young light. The target patterns are statistically significantly enriched in one of two classes of objects. Our method corrects for multiple hypothesis testing and correlations between patterns via the Westfall-Young permutation procedure, which empirically estimates the null distribution of pattern frequencies in each class via permutations. In our experiments, Westfall-Young light dramatically outperforms the current state-of-the-art approach, both in terms of runtime and memory efficiency on popular real-world benchmark datasets for pattern mining. The key to this efficiency is that, unlike all existing methods, our algorithm does not need to solve the underlying frequent pattern mining problem anew for each permutation and does not need to store the occurrence list of all frequent patterns. Westfall-Young light opens the door to significant pattern mining on large datasets that previously involved prohibitive runtime or memory costs. Our code is available from http://www.bsse.ethz.ch/mlcb/research/machine-learning/wylight.html
SPuManTE: Significant pattern mining with unconditional testing
Authors: Leonardo Pellegrina, Matteo Riondato, Fabio Vandin
Venue: Conference KDD 2019
Year: 2019

We present SPuManTE, an efficient algorithm for mining significant patterns from a transactional dataset. SPuManTE controls the Family-wise Error Rate: it ensures that the probability of reporting one or more false discoveries is less than an user-specified threshold. A key ingredient of SPuManTE is UT, our novel unconditional statistical test for evaluating the significance of a pattern, that requires fewer assumptions on the data generation process and is more appropriate for a knowledge discovery setting than classical conditional tests, such as the widely used Fisher's exact test. Computational requirements have limited the use of unconditional tests in significant pattern discovery, but UT overcomes this issue by obtaining the required probabilities in a novel efficient way. SPuManTE combines UT with recent results on the supremum of the deviations of pattern frequencies from their expectations, founded in statistical learning theory. This combination allows SPuManTE to be very efficient, while also enjoying high statistical power. The results of our experimental evaluation show that SPuManTE allows the discovery of statistically significant patterns while properly accounting for uncertainties in patterns' frequencies due to the data generation process.
Enhancing Badminton Player Performance via a Closed-Loop AI Approach: Imitation, Simulation, Optimization, and Execution
Authors: Kuang-Da Wang
Venue: Conference CIKM 2023
Year: 2023

In recent years, the sports industry has witnessed a significant rise in interest in leveraging artificial intelligence to enhance players' performance. However, the application of deep learning to improve badminton athletes' performance faces challenges related to identifying weaknesses, generating winning suggestions, and validating strategy effectiveness. These challenges arise due to the limited availability of realistic environments and agents. This paper aims to address these research gaps and make contributions to the badminton community. To achieve this goal, we propose a closed-loop approach consisting of six key components: Badminton Data Acquisition, Imitating Players' Styles, Simulating Matches, Optimizing Strategies, Training Execution, and Real-World Competitions. Specifically, we developed a novel model called RallyNet, which excels at imitating players' styles, allowing agents to accurately replicate real players' behavior. Secondly, we created a sophisticated badminton simulation environment that incorporates real-world physics, faithfully recreating game situations. Thirdly, we employed reinforcement learning techniques to improve players' strategies, enhancing their chances of winning while preserving their unique playing styles. By comparing strategy differences before and after improvement, we provide winning suggestions to players, which can be validated against diverse opponents within our carefully designed environment. Lastly, through collaborations with badminton venues and players, we apply the generated suggestions to the players' training and competitions, ensuring the effectiveness of our approach. Moreover, we continuously gather data from training and competitions, incorporating it into the closed-loop cycle to refine strategies and suggestions. This research presents an innovative approach for continuously improving players' performance, contributing to the field of AI-driven sports performance enhancement. This dissertation is supervised by Wen-Chih Peng (wcpeng@cs.nycu.edu.tw) and Ping-Chun Hsieh (pinghsieh@nycu.edu.tw).
PERD: Personalized Emoji Recommendation with Dynamic User Preference
Authors: Xuanzhi Zheng, Guoshuai Zhao, Li Zhu, Xueming Qian
Venue: Conference SIGIR 2022
Year: 2022

Emoji recommendation is an important task to help users find appropriate emojis from thousands of candidates based on a short tweet text. Traditional emoji recommendation methods lack personalized recommendation and ignore user historical information in selecting emojis. In this paper, we propose a personalized emoji recommendation with dynamic user preference (PERD) which contains a text encoder and a personalized attention mechanism. In text encoder, a BERT model is contained to learn dense and low-dimensional representations of tweets. In personalized attention, user dynamic preferences are learned according to semantic and sentimental similarity between historical tweets and the tweet which is waiting for emoji recommendation. Informative historical tweets are selected and highlighted. Experiments are carried out on two real-world datasets from Sina Weibo and Twitter. Experimental results validate the superiority of our approach on personalized emoji recommendation.
Bid landscape forecasting in online ad exchange marketplace
Authors: Ying Cui, Ruofei Zhang, Wei Li, Jianchang Mao
Venue: Conference KDD 2011
Year: 2011

Display advertising has been a significant source of revenue for publishers and ad networks in online advertising ecosystem. One important business model in online display advertising is Ad Exchange marketplace, also called non-guaranteed delivery (NGD), in which advertisers buy targeted page views and audiences on a spot market through real-time auction. In this paper, we describe a bid landscape forecasting system in NGD marketplace for any advertiser campaign specified by a variety of targeting attributes. In the system, the impressions that satisfy the campaign targeting attributes are partitioned into multiple mutually exclusive samples. Each sample is one unique combination of quantified attribute values. We develop a divide-and-conquer approach that breaks down the campaign-level forecasting problem. First, utilizing a novel star-tree data structure, we forecast the bid for each sample using non-linear regression by gradient boosting decision trees. Then we employ a mixture-of-log-normal model to generate campaign-level bid distribution based on the sample-level forecasted distributions. The experiment results of a system developed with our approach show that it can accurately forecast the bid distributions for various campaigns running on the world's largest NGD advertising exchange system, outperforming two baseline methods in term of forecasting errors.
Deep Censored Learning of the Winning Price in the Real Time Bidding
Authors: Wush Chi-Hsuan Wu, Mi-Yen Yeh, Ming-Syan Chen
Venue: Conference KDD 2018
Year: 2018

We generalize the winning price model to incorporate the deep learning models with different distributions and propose an algorithm to learn from the historical bidding information, where the winning price are either observed or partially observed. We study if the successful deep learning models of the click-through rate can enhance the prediction of the winning price or not. We also study how different distributions of winning price can affect the learning results. Experiment results show that the deep learning models indeed boost the prediction quality when they are learned on the historical observed data. In addition, the deep learning models on the unobserved data are improved after learning from the censored data. The main advantage of the proposed generalized deep learning model is to provide more flexibility to model the winning price and improve the performance in consideration of the possibly various winning price distributions and various model structures in practice.
MEOW: A Space-Efficient Nonparametric Bid Shading Algorithm
Authors: Wei Zhang, Brendan Kitts, Yanjun Han, Zhengyuan Zhou, Tingyu Mao, Hao He, Shengjun Pan, Aaron Flores, San Gultekin, Tsachy Weissman
Venue: Conference KDD 2021
Year: 2021

Bid Shading has become increasingly important in Online Advertising, with a large amount of commercial [4,12,13,29] and research work [11,20,28] recently published. Most approaches for solving the bid shading problem involve estimating the probability of win distribution, and then maximizing surplus [28]. These generally use parametric assumptions for the distribution, and there has been some discussion as to whether Log-Normal, Gamma, Beta, or other distributions are most effective [8,38,41,44]. In this paper, we show evidence that online auctions generally diverge in interesting ways from classic distributions. In particular, real auctions generally exhibit significant structure, due to the way that humans set up campaigns and inventory floor prices [16,26]. Using these insights, we present a nonparametric method for Bid Shading which enables the exploitation of this deep structure. The algorithm has low time and space complexity, and is designed to operate within the challenging millisecond Service Level Agreements of Real-Time Bid Servers. We deploy it in one of the largest Demand Side Platforms in the United States, and show that it reliably out-performs best in class Parametric benchmarks. We conclude by suggesting some ways that the best aspects of parametric and nonparametric approaches could be combined.