OpenRaiser/Intern-Atlas huggingface dataset:
Sos: Score-based oversampling for tabular data
Authors: Jayoung Kim 0002, Chaejeong Lee, Yehjin Shin, Sewon Park 0004, Minjung Kim, Noseong Park, Jihoon Cho
Venue: Conference KDD 2022
Year: 2022

Score-based generative models (SGMs) are a recent breakthrough in generating fake images. SGMs are known to surpass other generative models, e.g., generative adversarial networks (GANs) and variational autoencoders (VAEs). Being inspired by their big success, in this work, we fully customize them for generating fake tabular data. In particular, we are interested in oversampling minor classes since imbalanced classes frequently lead to sub-optimal training outcomes. To our knowledge, we are the first presenting a score-based tabular data oversampling method. Firstly, we re-design our own score network since we have to process tabular data. Secondly, we propose two options for our generation method: the former is equivalent to a style transfer for tabular data and the latter uses the standard generative policy of SGMs. Lastly, we define a fine-tuning method, which further enhances the oversampling quality. In our experiments with 6 datasets and 10 baselines, our method outperforms other oversampling methods in all cases.
Copycatch: stopping group attacks by spotting lockstep behavior in social networks
Authors: Alex Beutel, Wanhong Xu, Venkatesan Guruswami, Christopher Palow, Christos Faloutsos
Venue: Conference WWW 2013
Year: 2013

How can web services that depend on user generated content discern fraudulent input by spammers from legitimate input? In this paper we focus on the social network Facebook and the problem of discerning ill-gotten Page Likes, made by spammers hoping to turn a profit, from legitimate Page Likes. Our method, which we refer to as CopyCatch, detects lockstep Page Like patterns on Facebook by analyzing only the social graph between users and Pages and the times at which the edges in the graph (the Likes) were created. We offer the following contributions: (1) We give a novel problem formulation, with a simple concrete definition of suspicious behavior in terms of graph structure and edge constraints. (2) We offer two algorithms to find such suspicious lockstep behavior - one provably-convergent iterative algorithm and one approximate, scalable MapReduce implementation. (3) We show that our method severely limits "greedy attacks" and analyze the bounds from the application of the Zarankiewicz problem to our setting. Finally, we demonstrate and discuss the effectiveness of CopyCatch at Facebook and on synthetic data, as well as potential extensions to anomaly detection problems in other domains. CopyCatch is actively in use at Facebook, searching for attacks on Facebook's social graph of over a billion users, many millions of Pages, and billions of Page Likes.
Detection of novel social bots by ensembles of specialized classifiers
Authors: Mohsen Sayyadiharikandeh, Onur Varol, Kai-Cheng Yang, Alessandro Flammini, Filippo Menczer
Venue: Conference CIKM 2020
Year: 2020

Malicious actors create inauthentic social media accounts controlled in part by algorithms, known as social bots, to disseminate misinformation and agitate online discussion. While researchers have developed sophisticated methods to detect abuse, novel bots with diverse behaviors evade detection. We show that different types of bots are characterized by different behavioral features. As a result, supervised learning techniques suffer severe performance deterioration when attempting to detect behaviors not observed in the training data. Moreover, tuning these models to recognize novel bots requires retraining with a significant amount of new annotations, which are expensive to obtain. To address these issues, we propose a new supervised learning method that trains classifiers specialized for each class of bots and combines their decisions through the maximum rule. The ensemble of specialized classifiers (ESC) can better generalize, leading to an average improvement of 56\% in F1 score for unseen accounts across datasets. Furthermore, novel bot behaviors are learned with fewer labeled examples during retraining. We deployed ESC in the newest version of Botometer, a popular tool to detect social bots in the wild, with a cross-validation AUC of 0.99.
GAM: Explainable Visual Similarity and Classification via Gradient Activation Maps
Authors: Oren Barkan, Omri Armstrong, Amir Hertz, Avi Caciularu, Ori Katz, Itzik Malkiel, Noam Koenigstein
Venue: Conference CIKM 2021
Year: 2021

We present Gradient Activation Maps (GAM) - a machinery for explaining predictions made by visual similarity and classification models. By gleaning localized gradient and activation information from multiple network layers, GAM offers improved visual explanations, when compared to existing alternatives. The algorithmic advantages of GAM are explained in detail, and validated empirically, where it is shown that GAM outperforms its alternatives across various tasks and datasets.
Positive, Negative and Neutral: Modeling Implicit Feedback in Session-Based News Recommendation
Authors: Shansan Gong, Kenny Q. Zhu
Venue: Conference SIGIR 2022
Year: 2022

News recommendation for anonymous readers is a useful but challenging task for many news portals, where interactions between readers and articles are limited within a temporary login session. Previous works tend to formulate session-based recommendation as a next item prediction task, while they neglect the implicit feedback from user behaviors, which indicates what users really like or dislike. Hence, we propose a comprehensive framework to model user behaviors through positive feedback (i.e., the articles they spend more time on) and negative feedback (i.e., the articles they choose to skip without clicking in). Moreover, the framework implicitly models the user using their session start time, and the article using its initial publishing time, in what we call "neutral feedback". Empirical evaluation on three real-world news datasets shows the framework's promising performance of more accurate, diverse and even unexpectedness recommendations than other state-of-the-art session-based recommendation approaches.
A Category- Aware Multi-Interest Model for Personalized Product Search
Authors: Jiongnan Liu, Zhicheng Dou, Qiannan Zhu, Ji-Rong Wen
Venue: Conference WWW 2022
Year: 2022

Product search has been an important way for people to find products on online shopping platforms. Existing approaches in personalized product search mainly embed user preferences into one single vector. However, this simple strategy easily results in sub-optimal representations, failing to model and disentangle user’s multiple preferences. To overcome this problem, we proposed a category-aware multi-interest model to encode users as multiple preference embeddings to represent user-specific interests. Specifically, we also capture the category indications for each preference to indicate the distribution of categories it focuses on, which is derived from rich relations between users, products, and attributes. Based on these category indications, we develop a category attention mechanism to aggregate these various preference embeddings considering current queries and items as the user’s comprehensive representation. By this means, we can use this representation to calculate matching scores of retrieved items to determine whether they meet the user’s search intent. Besides, we introduce a homogenization regularization term to avoid the redundancy between user interests. Experimental results show that the proposed method significantly outperforms existing approaches.
Learning a Joint Search and Recommendation Model from User-Item Interactions
Authors: Hamed Zamani, W. Bruce Croft
Venue: Conference WSDM 2020
Year: 2020

Existing learning to rank models for information retrieval are trained based on explicit or implicit query-document relevance information. In this paper, we study the task of learning a retrieval model based on user-item interactions. Our model has potential applications to the systems with rich user-item interaction data, such as browsing and recommendation, in which having an accurate search engine is desired. This includes media streaming services and e-commerce websites among others. Inspired by the neural approaches to collaborative filtering and the language modeling approaches to information retrieval, our model is jointly optimized to predict user-item interactions and reconstruct the item textual descriptions. In more details, our model learns user and item representations such that they can accurately predict future user-item interactions, while generating an effective unigram language model for each item. Our experiments on four diverse datasets in the context of movie and product search and recommendation demonstrate that our model substantially outperforms competitive retrieval baselines, in addition to providing comparable performance to state-of-the-art hybrid recommendation models.
Invariant Graph Learning for Causal Effect Estimation
Authors: Yongduo Sui, Caizhi Tang, Zhixuan Chu, Junfeng Fang, Yuan Gao, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang
Venue: Conference WWW 2024
Year: 2024

Causal effect estimation from networked observational data encounters notable challenges, primarily hidden confounders arising from network structure, or spillover effects that influence unit's outcomes based on neighboring treatment assignments. Existing graph neural network (GNN)-based methods have endeavored to address these challenges, utilizing the GNN's message-passing mechanism to capture hidden confounders or model spillover effects. However, they mainly focus on transductive causal effect learning on a single networked data, limiting their efficacy in inductive settings for real-world applications where networked data often originates from multiple environments influenced by potentially varying time or geographical regions. In light of this, we introduce the principle of invariance to the task of causal effect estimation on networked data, culminating in our Invariant Graph Learning (IGL) framework. Specifically, it first generates multiple networked data to simulate diverse environments from a given observational data. Then it further encourages the model to learn environment-invariant representations for confounders and spillover effects. Such a design enables the model to extrapolate beyond a single observed environment, thereby improving the performance of causal effect estimation in potential new environments. Extensive experiments on two real-world datasets demonstrates the superiority of our approach.
Diff-MSR: A Diffusion Model Enhanced Paradigm for Cold-Start Multi-Scenario Recommendation
Authors: Yuhao Wang, Ziru Liu, Yichao Wang, Xiangyu Zhao, Bo Chen, Huifeng Guo, Ruiming Tang
Venue: Conference WSDM 2024
Year: 2024

With the explosive growth of various commercial scenarios, there is an increasing number of studies on multi-scenario recommendation (MSR) which trains the recommender system with the data from multiple scenarios, aiming to improve the recommendation performance on all these scenarios synchronously. However, due to the large discrepancy in the number of interactions among domains, multi-scenario recommendation models usually suffer from insufficient learning and negative transfer especially on the cold-start scenarios, thus exacerbating the data sparsity issue. To fill this gap, in this work we propose a novel diffusion model enhanced paradigm tailored for the cold-start problem in multi-scenario recommendation in a data-driven generative manner. Specifically, based on all-domain data, we leverage the diffusion model with our newly designed variance schedule and the proposed classifier, which explicitly boosts the recommendation performance on the cold-start scenarios by exploiting the generated high-quality and informative embedding, leveraging the abundance of rich scenarios. Our experiments on Douban and Amazon datasets demonstrate two strengths of the proposed paradigm: (i) its effectiveness with a significant increase of 8.5% and 1% in accuracy on the two datasets, and (ii) its compatibility with various multi-scenario backbone models. The implementation code is available for easy reproduction.
Commonsense properties from query logs and question answering forums
Authors: Julien Romero, Simon Razniewski, Koninika Pal, Jeff Z. Pan, Archit Sakhadeo, Gerhard Weikum
Venue: Conference CIKM 2019
Year: 2019

Commonsense knowledge about object properties, human behavior and general concepts is crucial for robust AI applications. However, automatic acquisition of this knowledge is challenging because of sparseness and bias in online sources. This paper presents Quasimodo, a methodology and tool suite for distilling commonsense properties from non-standard web sources. We devise novel ways of tapping into search-engine query logs and QA forums, and combining the resulting candidate assertions with statistical cues from encyclopedias, books and image tags in a corroboration step. Unlike prior work on commonsense knowledge bases, Quasimodo focuses on salient properties that are typically associated with certain objects or concepts. Extensive evaluations, including extrinsic use-case studies, show that Quasimodo provides better coverage than state-of-the-art baselines with comparable quality.
Oblique Decision Trees from Derivatives of ReLU Networks
Authors: Guang-He Lee, Tommi S. Jaakkola
Venue: Conference ICLR 2020
Year: 2020

We show how neural models can be used to realize piece-wise constant functions such as decision trees. The proposed architecture, which we call locally constant networks, builds on ReLU networks that are piece-wise linear and hence their associated gradients with respect to the inputs are locally constant. We formally establish the equivalence between the classes of locally constant networks and decision trees. Moreover, we highlight several advantageous properties of locally constant networks, including how they realize decision trees with parameter sharing across branching / leaves. Indeed, only $M$ neurons suffice to implicitly model an oblique decision tree with $2^M$ leaf nodes. The neural representation also enables us to adopt many tools developed for deep networks (e.g., DropConnect (Wan et al., 2013)) while implicitly training decision trees. We demonstrate that our method outperforms alternative techniques for training oblique decision trees in the context of molecular property classification and regression tasks.
How Important is Periodic Model Update in Recommender System?
Authors: Hyunsung Lee, Sungwook Yoo, Dongjun Lee, Jaekwang Kim
Venue: Conference SIGIR 2023
Year: 2023

In real-world recommender model deployments, the models are typically retrained and deployed repeatedly. It is the rule-of-thumb to periodically retrain recommender models to capture up-to-date user behavior and item trends. However, the harm caused by delayed model updates has not been investigated extensively yet. in this perspective paper, we formulate the delayed model update problem and quantitatively demonstrate the delayed model update actually harms the model performance by increasing the number of cold users and cold items increase and decreasing overall model performances. These effects vary across different domains having different characteristics. Upon these findings, we further argue that although the delayed model update has negative effects on online recommender model deployment, yet it has not gathered enough attention from research communities. We argue our verification of the relationship between the model update cycle and model performance calls for further research such as faster model training, and more efficient data pipelines to keep the model more up-to-date with the latest user behaviors and item trends.
A comparison of indexing techniques for Japanese text retrieval
Authors: Hideo Fujii, W. Bruce Croft
Venue: Conference SIGIR 1993
Year: 1993

A series of Japanese full-text retrieval experiments were conducted using an inference network document retrieval model. The retrieval performance of two major indexing methods, character-based and word-based, were evaluated. Using structured queries, the character-based indexing performed retrieval as well as, or slightly better, than the word-based system. This result has practical significance since the character-based indexing speed is considerably faster than the traditional word-based indexing. All the queries in this experiment were automatically formulated from natural language input.
Approaches to passage retrieval in full text information systems
Authors: Gerard Salton, James Allan 0001, Chris Buckley
Venue: Conference SIGIR 1993
Year: 1993

Large collections of full-text documents are now commonly used in automated information retrieval. When the stored document texts are long, the retrieval of complete documents may not be in the users' best interest. In such circumstance, efficient and effective retrieval results may be obtained by using passage retrieval strategies designed to retrieve text excerpts of varying size in response to statements of user interest.
Integrating word relationships into language models
Authors: Guihong Cao, Jian-Yun Nie, Jing Bai
Venue: Conference SIGIR 2005
Year: 2005

In this paper, we propose a novel dependency language modeling approach for information retrieval. The approach extends the existing language modeling approach by relaxing the independence assumption. Our goal is to build a language model in which various word relationships can be integrated. In this work, we integrate two types of relationship extracted from WordNet and co-occurrence relationships respectively. The integrated model has been tested on several TREC collections. The results show that our model achieves substantial and significant improvements with respect to the models without these relationships. These results clearly show the benefit of integrating word relationships into language models for IR.
Information shared by many objects
Authors: Chong Long, Xiaoyan Zhu, Ming Li, Bin Ma
Venue: Conference CIKM 2008
Year: 2008

If Kolmogorov complexity [25] measures information in one object and Information Distance measures information shared by two objects, how do we measure information shared by many objects? This paper provides an initial pragmatic study of this fundamental data mining question. Firstly, Em(x1,x2,...,xn) is defined to be the minimum amount of thermodynamic energy needed to convert from any xi to any xj. With this definition several theoretical problems have been solved. Second, our newly proposed theory is applied to select a comprehensive review and a specialized review from many reviews: (1) Core feature words, expanded words and dependent words are extracted respectively. (2) Comprehensive and specialized reviews are selected according to the information among them. This method of selecting a single review can be extended to select multiple reviews as well. Finally, experiments show that this comprehensive and specialized review mining method based on our new theory can do the job efficiently.
Semantic term matching in axiomatic approaches to information retrieval
Authors: Hui Fang, ChengXiang Zhai
Venue: Conference SIGIR 2006
Year: 2006

A common limitation of many retrieval models, including the recently proposed axiomatic approaches, is that retrieval scores are solely based on exact (i.e., syntactic) matching of terms in the queries and documents, without allowing distinct but semantically related terms to match each other and contribute to the retrieval score. In this paper, we show that semantic term matching can be naturally incorporated into the axiomatic retrieval model through defining the primitive weighting function based on a semantic similarity function of terms. We define several desirable retrieval constraints for semantic term matching and use such constraints to extend the axiomatic model to directly support semantic term matching based on the mutual information of terms computed on some document set. We show that such extension can be efficiently implemented as query expansion. Experiment results on several representative data sets show that, with mutual information computed over the documents in either the target collection for retrieval or an external collection such as the Web, our semantic expansion consistently and substantially improves retrieval accuracy over the baseline axiomatic retrieval model. As a pseudo feedback method, our method also outperforms a state-of-the-art language modeling feedback method.
Efficient Multi-word Expressions Extractor Using Suffix Arrays and Related Structures
Authors: José Aires, Gabriel Pereira Lopes, Joaquim Ferreira da Silva
Venue: Conference CIKM 2008
Year: 2008

For Information Retrieval purposes, there is a need for regularly processing predictably dynamic and potentially huge corpora for extraction of contiguous Multi Word Expressions (MWEs), in a way that should be computationally tractable. In this paper we'll be mainly exploring the use of Suffix Arrays, together with the SCP association measure and the Smoothed LocalMaxs algorithm. The choice of Suffix Arrays and the construction of auxiliary structures enabled a clear minimization of the time for extracting multi-word expressions, with linear complexity by the introduction of a limitation on the number of words. Despite the methodology being essentially of a statistical nature, we show how to handle hybrid extraction mechanisms.
A testbed for people searching strategies in the www
Authors: Javier Artiles, Julio Gonzalo, Felisa Verdejo
Venue: Conference SIGIR 2005
Year: 2005

This paper describes the creation of a testbed to evaluate people searching strategies on the World-Wide-Web. This task involves resolving person names' ambiguity and locating relevant information characterising every individual under the same name.
An algorithm for text categorization
Authors: Anestis Gkanogiannis, Theodore Kalamboukis
Venue: Conference SIGIR 2008
Year: 2008

A novel and efficient learning algorithm is proposed for the binary linear classification problem. The algorithm is trained using the Rocchio's relevance feedback technique and builds a classifier by the intermediate hyperplane of two common tangent hyperplanes for the given category and its complement. Experimental results presented are very encouraging and justify the need for further research.
Improving Automatic Query Expansion
Authors: Mandar Mitra, Amit Singhal, Chris Buckley
Venue: Conference SIGIR 1998
Year: 1998

Most casual users of IR systems type short queries. Recent research has shown that adding new words to these queries via blind feedback, without any input from the user, improves the performance of such queries. We investigate ways to improve this query expansion process by refining the set of documents used in feedback. We start by using manually formulated Boolean filters along with proximity constraints. Our approach is similar to the one proposed in [10]. Next, we investigate a completely automatic method that makes use of term cooccurrence information to estimate word correlation. Results show that refining the set of documents used in query expansion yields substantial improvements in retrieval effectiveness, both in terms of average precision and precision at top twenty documents. Such refinement often prevents the query drift caused by blind expansion. More importantly, the fully automatic approach developed in this study performs competitively with the best manual approach and...