OpenRaiser/Intern-Atlas huggingface dataset:
Disasternet: Causal bayesian networks with normalizing flows for cascading hazards estimation from satellite imagery
Authors: Xuechun Li, Paula M. Bürgi, Wei Ma 0016, Hae Young Noh, David Jay Wald, Susu Xu
Venue: Conference KDD 2023
Year: 2023

Sudden-onset hazards like earthquakes often induce cascading secondary hazards (e.g., landslides, liquefaction, debris flows, etc.) and subsequent impacts (e.g., building and infrastructure damage) that cause catastrophic human and economic losses. Rapid and accurate estimates of these hazards and impacts are critical for timely and effective post-disaster responses. Emerging remote sensing techniques provide pre- and post-event satellite images for rapid hazard estimation. However, hazards and damage often co-occur or colocate with underlying complex cascading geophysical processes, making it challenging to directly differentiate multiple hazards and impacts from satellite imagery using existing single-hazard models. We introduce DisasterNet, a novel family of causal Bayesian networks to model processes that a major hazard triggers cascading hazards and impacts and further jointly induces signal changes in remotely sensed observations. We integrate normalizing flows to effectively model the highly complex causal dependencies in this cascading process. A triplet loss is further designed to leverage prior geophysical knowledge to enhance the identifiability of our highly expressive Bayesian networks. Moreover, a novel stochastic variational inference with normalizing flows is derived to jointly approximate posteriors of multiple unobserved hazards and impacts from noisy remote sensing observations. Integrating with the USGS Prompt Assessment of Global Earthquakes for Response (PAGER) system, our framework is evaluated in recent global earthquake events. Evaluation results show that DisasterNet significantly improves multiple hazard and impact estimation compared to existing USGS products.
Efficient Construction of large test collections
Authors: Gordon V. Cormack, Christopher R. Palmer, Charles L. A. Clarke
Venue: Conference SIGIR 1998
Year: 1998

Test collections with a million or more documents are needed for the evaluation of modern information retrieval systems. Yet their construction requires a great deal of effort. Judgements must be rendered as to whether or not documents are relevant to each of a set of queries. Exhaustive judging, in which every document is examined and a judgement rendered, is infeasible for collections of this size. Current practice is represented by the "pooling method", as used in the TREC conference series, in which only the first k documents from each of a number of sources are judged. We propose two methods, Intemctive Searching and Judging and Moveto-front Pooling, that yield effective test collections while requiring many fewer judgements. Interactive Searching and Judging selects documents to be judged using an interactive search system, and may be used by a small research team to develop an effective test collection using minimal resources. Move-to-Front Pooling directly improves on the standard pooling method by using a variable number of documents from each source depending on its retrieval performance. Move-to-Front Pooling would be an appropriate replacement for the standard pooling method in future collection development efforts involving many independent groups.
Hypothesis testing with incomplete relevance judgments
Authors: Ben Carterette, Mark D. Smucker
Venue: Conference CIKM 2007
Year: 2007

Information retrieval experimentation generally proceeds in a cycle of development, evaluation, and hypothesis testing. Ideally, the evaluation and testing phases should be short and easy, so as to maximize the amount of time spent in development. There has been recent work on reducing the amount of assessor effort needed to evaluate retrieval sys-tems, but it has not, for the most part, investigated the ef-fects of these methods on tests of significance. In this work, we explore in detail the effects of reduced sets of judgments on the sign test. We demonstrate both analytically and em-pirically the relationship between the power of the test, the number of topics evaluated, and the number of judgments available. Using these relationships, we can determine the number of topics and judgments needed for the least-cost but highest-confidence significance evaluation. Specifically, testing pairwise significance over 192 topics with fewer than 5 judgments for each is as good as testing significance over 25 topics with an average of 166 judgments for each—85% less effort producing no additional errors.
Thymeflow, a personal knowledge base with spatio-temporal data
Authors: David Montoya, Thomas Pellissier Tanon, Serge Abiteboul, Fabian M. Suchanek
Venue: Conference CIKM 2016
Year: 2016

The typical Internet user has data spread over several devices and across several online systems. We demonstrate an open-source system for integrating user's data from different sources into a single Knowledge Base. Our system integrates data of different kinds into a coherent whole, starting with email messages, calendar, contacts, and location history. It is able to detect event periods in the user's location data and align them with calendar events. We will demonstrate how to query the system within and across different dimensions, and perform analytics over emails, events, and locations.
Correcting Knowledge Base Assertions
Authors: Jiaoyan Chen, Xi Chen, Ian Horrocks, Erik B. Myklebust, Ernesto Jiménez-Ruiz
Venue: Conference WWW 2020
Year: 2020

The usefulness and usability of knowledge bases (KBs) is often limited by quality issues. One common issue is the presence of erroneous assertions, often caused by lexical or semantic confusion. We study the problem of correcting such assertions, and present a general correction framework which combines lexical matching, semantic embedding, soft constraint mining and semantic consistency checking. The framework is evaluated using DBpedia and an enterprise medical KB.
Carousel ads optimization in yahoo gemini native
Authors: Michal Aharon, Oren Somekh, Avi Shahar, Assaf Singer, Baruch Trayvas, Hadas Vogel, Dobri Dobrev
Venue: Conference KDD 2019
Year: 2019

Yahoo's native advertising (also known as Gemini native) serves billions of ad impressions daily, reaching a yearly run-rate of many hundred of millions USD. Driving Gemini native models for predicting both click probability (pCTR) and conversion probability (pCONV) is OFFSET - a feature enhanced collaborative-filtering (CF) based event prediction algorithm. The predicted pCTRs are then used in Gemini native auctions to determine which ads to present for each serving event. A fast growing segment of Gemini native is Carousel ads that include several cards (or assets) which are used to populate several slots within the ad. Since Carousel ad slots are not symmetrical and some are more conspicuous than others, it is beneficial to render assets to slots in a way that maximizes revenue. In this work we present a post-auction successive elimination based approach for ranking assets according to their click trough rate (CTR) and render the carousel accordingly, placing higher CTR assets in more conspicuous slots. After a successful online bucket showing 8.6% CTR and 4.3% CPM (or revenue) lifts over a control bucket that uses predefined advertisers assets-to-slots mapping, the carousel asset optimization (CAO) system was pushed to production and is serving all Gemini native traffic since. A few months after CAO deployment, we have already measured an almost 40% increase in carousel ads revenue. Moreover, the entire revenue growth is related to CAO traffic increase due to additional advertiser demand, which demonstrates a high advertisers' satisfaction of the product.
Feature enhancement via user similarities networks for improved click prediction in yahoo gemini native
Authors: Morelle Arian, Eliran Abutbul, Michal Aharon, Yair Koren, Oren Somekh, Rotem Stram
Venue: Conference CIKM 2019
Year: 2019

Yahoo's native advertising marketplace (also known as Gemini native) serves billions of ad impressions daily, reaching many hundreds of millions USD in yearly revenue. Driving Gemini native models that are used to predict ad click probability (pCTR) is OFFSET - a feature enhanced collaborative-filtering (CF) based event prediction algorithm. While some of the user features used by OFFSET have high coverage, other features, especially those based on click patterns, suffer from extremely low coverage. In this work, we present a framework that simplifies complex interactions between users and other entities in a bipartite graph. The one mode projection of this bipartite graph onto users represents a user similarity network, allowing us to quantify similarities between users. This network is combined with existing user features to create an enhanced feature set. In particular, we describe the implementation and performance of our framework using user Internet browsing data (e.g., visited pages URLs) to enhance the user category feature. Using our framework we effectively increase the feature coverage by roughly 15%. Moreover, online results evaluated on 1% of Gemini native traffic show that using the enhanced feature increases revenue by almost 1% when compared to the baseline operating with the original feature, which is a substantial increase at scale.
Evaluating Diversified Search Results Using Per-Intent Graded Relevance
Authors: Tetsuya Sakai, Ruihua Song
Venue: Conference SIGIR 2011
Year: 2011

Search queries are often ambiguous and/or underspecified. To accomodate different user needs, search result diversification has received attention in the past few years. Accordingly, several new metrics for evaluating diversification have been proposed, but their properties are little understood. We compare the properties of existing metrics given the premises that (1) queries may have multiple intents; (2) the likelihood of each intent given a query is available; and (3) graded relevance assessments are available for each intent. We compare a wide range of traditional and diversified IR metrics after adding graded relevance assessments to the TREC 2009 Web track diversity task test collection which originally had binary relevance assessments. Our primary criterion is discriminative power, which represents the reliability of a metric in an experiment. Our results show that diversified IR experiments with a given number of topics can be as reliable as traditional IR experiments with the same number of topics, provided that the right metrics are used. Moreover, we compare the intuitiveness of diversified IR metrics by closely examining the actual ranked lists from TREC. We show that a family of metrics called D#-measures have several advantages over other metrics such as α-nDCG and Intent-Aware metrics.
Butterfly counting in bipartite networks
Authors: Seyed-Vahid Sanei-Mehri, Ahmet Erdem Sariyüce, Srikanta Tirthapura
Venue: Conference KDD 2018
Year: 2018

We consider the problem of counting motifs in bipartite affiliation networks, such as author-paper, user-product, and actor-movie relations. We focus on counting the number of occurrences of a "butterfly", a complete $2 \times 2$ biclique, the simplest cohesive higher-order structure in a bipartite graph. Our main contribution is a suite of randomized algorithms that can quickly approximate the number of butterflies in a graph with a provable guarantee on accuracy. An experimental evaluation on large real-world networks shows that our algorithms return accurate estimates within a few seconds, even for networks with trillions of butterflies and hundreds of millions of edges.
Efficiently Counting Vertex Orbits of All 5-vertex Subgraphs, by EVOKE
Authors: Noujan Pashanasangi, C. Seshadhri
Venue: Conference WSDM 2020
Year: 2020

Subgraph counting is a fundamental task in network analysis. Typically, algorithmic work is on total counting, where we wish to count the total frequency of a (small) pattern subgraph in a large input data set. But many applications require local counts (also called vertex orbit counts) wherein, for every vertex v of the input graph, one needs the count of the pattern subgraph involving v. This provides a rich set of vertex features that can be used in machine learning tasks, especially classification and clustering. But getting local counts is extremely challenging. Even the easier problem of getting total counts has received much research attention. Local counts require algorithms that get much finer grained information, and the sheer output size makes it difficult to design scalable algorithms. We present EVOKE, a scalable algorithm that can determine vertex orbits counts for all 5-vertex pattern subgraphs. In other words, EVOKE exactly determines, for every vertex v of the input graph and every 5-vertex subgraph H, the number of copies of H that v participates in. EVOKE can process graphs with tens of millions of edges, within an hour on a commodity machine. EVOKE is typically hundreds of times faster than previous state of the art algorithms, and gets results on datasets beyond the reach of previous methods. Theoretically, we generalize a recent "graph cutting" framework to get vertex orbit counts. This framework generate a collection of polynomial equations relating vertex orbit counts of larger subgraphs to those of smaller subgraphs. EVOKE carefully exploits the structure among these equations to rapidly count. We prove and empirically validate that EVOKE only has a small constant factor overhead over the best (total) 5-vertex subgraph counter.
Correcting for Recency Bias in Job Recommendation
Authors: Ruey-Cheng Chen, Qingyao Ai, Gaya Jayasinghe, W. Bruce Croft
Venue: Conference CIKM 2019
Year: 2019

Users are known to interact more with fresh content in certain temporally associated domains such as news search or job seeking, leading to an uneven distribution of interactions over items of different degrees of freshness. Data collected under such an "aging effect'' is usually used unconditionally on all sort of recommendation tasks, and as a result more recently published content may be over-represented during model training and evaluation. In this study, we characterize this temporal influence as a recency bias, and present an analysis in the domain of job recommendation. We show that, by correcting for recency bias using an unbiased learning to rank approach, one can improve the quality of recommendation significantly over a recent neural collaborative filtering model on RecSys Challenge 2017 data.
Review response generation in e-commerce platforms with external product information
Authors: Lujun Zhao, Kaisong Song, Changlong Sun, Qi Zhang, Xuanjing Huang, Xiaozhong Liu
Venue: Conference WWW 2019
Year: 2019

''User reviews” are becoming an essential component of e-commerce. When buyers write a negative or doubting review, ideally, the sellers need to quickly give a response to minimize the potential impact. When the number of reviews is growing at a frightening speed, there is an urgent need to build a response writing assistant for customer service providers. In order to generate high-quality responses, the algorithm needs to consume and understand the information from both the original review and the target product. The classical sequence-to-sequence (Seq2Seq) methods can hardly satisfy this requirement. In this study, we propose a novel deep neural network model based on the Seq2Seq framework for the review response generation task in e-commerce platforms, which can incorporate product information by a gated multi-source attention mechanism and a copy mechanism. Moreover, we employ a reinforcement learning technique to reduce the exposure bias problem. To evaluate the proposed model, we constructed a large-scale dataset from a popular e-commerce website, which contains product information. Empirical studies on both automatic evaluation metrics and human annotations show that the proposed model can generate informative and diverse responses, significantly outperforming state-of-the-art text generation models.
Explaining reviews and ratings with paco: Poisson additive co-clustering
Authors: Chao-Yuan Wu, Alex Beutel, Amr Ahmed, Alexander J. Smola
Venue: Conference WWW 2016
Year: 2016

Understanding a user's motivations provides valuable information beyond the ability to recommend items. Quite often this can be accomplished by perusing both ratings and review texts. Unfortunately matrix factorization approaches to recommendation result in large, complex models that are difficult to interpret. In this paper, we attack this problem through succinct additive co-clustering on both ratings and reviews. Our model yields accurate and interpretable recommendations.
Triangle-aware Spectral Sparsifiers and Community Detection
Authors: Konstantinos Sotiropoulos, Charalampos E. Tsourakakis
Venue: Conference KDD 2021
Year: 2021

Triangle-aware graph partitioning has proven to be a successful approach to finding communities in real-world data [8, 40, 51, 54]. But how can we explain its empirical success? Triangle-aware graph partitioning methods rely on the count of triangles an edge is contained in, in contrast to the well-established measure of effective resistance [12] that requires global information about the graph. In this work we advance the understanding of triangle-based graph partitioning in two ways. First, we introduce a novel triangle-aware sparsification scheme. Our scheme provably produces a spectral sparsifier with high probability [46, 47] on graphs that exhibit strong triadic closure, a hallmark property of real-world networks. Importantly, our sampling scheme is amenable to distributed computing, since it relies simply on computing node degrees, and edge triangle counts. Finally, we compare our methods to the Spielman-Srivastava sparsification algorithm [46] on a wide variety of real-world graphs, and we verify the applicability of our proposed sparsification scheme on real-world networks. Secondly, we develop a data-driven approach towards understanding properties of real-world communities with respect to effective resistances, and triangle counts. Our empirical approach is mainly based on the notion of ground-truth communities in datasets made available originally by Yang and Leskovec [53]. We perform a study of triangle-aware measures, and effective resistances on edges within, and across communities, and we discover certain interesting empirical findings. For example, we observe that the Jaccard similarity of an edge used by Satuluri [40], and the closely related Tectonic similarity measure introduced by Tsourakakis et al. [51] provide consistently good signals of whether an edge is contained within a community or not.
Algorithmic bias: do good systems make relevant documents more retrievable?
Authors: Colin Wilkie, Leif Azzopardi
Venue: Conference CIKM 2017
Year: 2017

Algorithmic bias presents a difficult challenge within Information Retrieval. Long has it been known that certain algorithms favour particular documents due to attributes of these documents that are not directly related to relevance. The evaluation of bias has recently been made possible through the use of retrievability, a quantifiable measure of bias. While evaluating bias is relatively novel, the evaluation of performance has been common since the dawn of the Cranfield approach and TREC. To evaluate performance, a pool of documents to be judged by human assessors is created from the collection. This pooling approach has faced accusations of bias due to the fact that the state of the art algorithms were used to create it, thus the inclusion of biases associated with these algorithms may be included in the pool. The introduction of retrievability has provided a mechanism to evaluate the bias of these pools. This work evaluates the varying degrees of bias present in the groups of relevant and non-relevant documents for topics. The differentiating power of a system is also evaluated by examining the documents from the pool that are retrieved for each topic. The analysis finds that the systems that perform better, tend to have a higher chance of retrieving a relevant document rather than a non-relevant document for a topic prior to retrieval, indicating that retrieval systems which perform better at TREC are already predisposed to agree with the judgements regardless of the query posed.
Personalized mobile app recommendation: Reconciling app functionality and user privacy preference
Authors: Bin Liu, Deguang Kong, Lei Cen, Neil Zhenqiang Gong, Hongxia Jin, Hui Xiong
Venue: Conference WSDM 2015
Year: 2015

Recent years have witnessed a rapid adoption of mobile devices and a dramatic proliferation of mobile applications (Apps for brevity). However, the large number of mobile Apps makes it difficult for users to locate relevant Apps. Therefore, recommending Apps becomes an urgent task. Traditional recommendation approaches focus on learning the interest of a user and the functionality of an item (e.g., an App) from a set of user-item ratings, and they recommend an item to a user if the item's functionality well matches the user's interest. However, Apps could have privileges to access a user's sensitive resources ( e.g., contact, message, and location). As a result, a user chooses an App not only because of its functionality, but also because it respects the user's privacy preference. To the best of our knowledge, this paper presents the first systematic study on incorporating both interest-functionality interactions and users' privacy preferences to perform personalized App recommendations. Specifically, we first construct a new model to capture the trade-off between functionality and user privacy preference. Then we crawled a real-world dataset (16,344 users, 6,157 Apps, and 263,054 ratings) from Google Play and use it to comprehensively evaluate our model and previous methods. We find that our method consistently and substantially outperforms the state-of-the-art approaches, which implies the importance of user privacy preference on personalized App recommendations. Moreover, we explore the impact of different levels of privacy information on the performances of our method, which gives us insights on what resources are more likely to be treated as private by users and influence users' behaviors at selecting Apps.
Social networks: looking ahead
Authors: Ravi Kumar, Alexander Tuzhilin, Christos Faloutsos, David D. Jensen, Gueorgi Kossinets, Jure Leskovec, Andrew Tomkins
Venue: Conference KDD 2008
Year: 2008

By now, online social networks have become an indispensable part of both online and offline lives of human beings. A large fraction of time spent online by a user is directly influence by the social networks to which he/she belongs. This calls for a deeper examination of social networks as large-scale dynamic objects that foster efficient person-person interaction.