OpenRaiser/Intern-Atlas huggingface dataset:
Mining features for sequence classification
Authors: Neal Lesh, Mohammed Javeed Zaki, Mitsunori Ogihara
Venue: Conference KDD 1999
Year: 1999

Classi cation algorithms are di cult to apply to sequential examples, such as plan executions or text, because there is a vast number of potentially useful features for describing each example. Past work on feature selection has focused on searching the space of all subsets of the available features which is intractable for large feature sets. We adapt data mining techniques to act as a preprocessor to select features for standard classi cation algorithms such as Naive Bayes and Winnow. We apply our algorithm to the task of predicting whether or not a plan will succeed or fail, during plan execution. The features produced by our algorithm improve classi cation accuracy by 10-50% in our experiments. Submitted to IJCAI'99. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonpro t educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Information Technology Center America; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Information Technology Center America. All rights reserved. Copyright c Mitsubishi Electric Information Technology Center America, 1998 201 Broadway, Cambridge, Massachusetts 02139 Rensselaer Polytechnic Institute University of Rochester Publication History:{ 1. First printing, TR-98-22, December 1998
Multilinear factorization machines for multi-task multi-view learning
Authors: Chun-Ta Lu, Lifang He, Weixiang Shao, Bokai Cao, Philip S. Yu
Venue: Conference WSDM 2017
Year: 2017

Many real-world problems, such as web image analysis, document categorization and product recommendation, often exhibit dual-heterogeneity: heterogeneous features obtained in multiple views, and multiple tasks might be related to each other through one or more shared views. To address these Multi-Task Multi-View (MTMV) problems, we propose a tensor-based framework for learning the predictive multilinear structure from the full-order feature interactions within the heterogeneous data. The usage of tensor structure is to strengthen and capture the complex relationships between multiple tasks with multiple views. We further develop efficient multilinear factorization machines (MFMs) that can learn the task-specific feature map and the task-view shared multilinear structures, without physically building the tensor. In the proposed method, a joint factorization is applied to the full-order interactions such that the consensus representation can be learned. In this manner, it can deal with the partially incomplete data without difficulty as the learning procedure does not simply rely on any particular view. Furthermore, the complexity of MFMs is linear in the number of parameters, which makes MFMs suitable to large-scale real-world problems. Extensive experiments on four real-world datasets demonstrate that the proposed method significantly outperforms several state-of-the-art methods in a wide variety of MTMV problems.
Frequent-subsequence-based prediction of outer membrane proteins
Authors: Rong She, Fei Chen, Ke Wang, Martin Ester, Jennifer L. Gardy, Fiona S. L. Brinkman
Venue: Conference KDD 2003
Year: 2003

A number of medically important disease-causing bacteria (collectively called Gram-negative bacteria) are noted for the extra "outer" membrane that surrounds their cell. Proteins resident in this membrane (outer membrane proteins, or OMPs) are of primary research interest for antibiotic and vaccine drug design as they are on the surface of the bacteria and so are the most accessible targets to develop new drugs against. With the development of genome sequencing technology and bioinformatics, biologists can now deduce all the proteins that are likely produced in a given bacteria and have attempted to classify where proteins are located in a bacterial cell. However such protein localization programs are currently least accurate when predicting OMPs, and so there is a current need for the development of a better OMP classifier. Data mining research suggests that the use of frequent patterns has good performance in aiding the development of accurate and efficient classification algorithms. In this paper, we present two methods to identify OMPs based on frequent subsequences and test them on all Gram-negative bacterial proteins whose localizations have been determined by biological experiments. One classifier follows an association rule approach, while the other is based on support vector machines (SVMs). We compare the proposed methods with the state-of-the-art methods in the biological domain. The results demonstrate that our methods are better both in terms of accurately identifying OMPs and providing biological insights that increase our understanding of the structures and functions of these important proteins.
Repeat buyer prediction for e-commerce
Authors: Guimei Liu, Tam T. Nguyen, Gang Zhao, Wei Zha, Jianbo Yang, Jianneng Cao, Min Wu, Peilin Zhao, Wei Chen
Venue: Conference KDD 2016
Year: 2016

A large number of new buyers are often acquired by merchants during promotions. However, many of the attracted buyers are one-time deal hunters, and the promotions may have little long-lasting impact on sales. It is important for merchants to identify who can be converted to regular loyal buyers and then target them to reduce promotion cost and increase the return on investment (ROI). At International Joint Conferences on Artificial Intelligence (IJCAI) 2015, Alibaba hosted an international competition for repeat buyer prediction based on the sales data of the ``Double 11" shopping event in 2014 at Tmall.com. We won the first place at stage 1 of the competition out of 753 teams. In this paper, we present our winning solution, which consists of comprehensive feature engineering and model training. We created profiles for users, merchants, brands, categories, items and their interactions via extensive feature engineering. These profiles are not only useful for this particular prediction task, but can also be used for other important tasks in e-commerce, such as customer segmentation, product recommendation, and customer base augmentation for brands. Feature engineering is often the most important factor for the success of a prediction task, but not much work can be found in the literature on feature engineering for prediction tasks in e-commerce. Our work provides some useful hints and insights for data science practitioners in e-commerce.
Constrained optimization for homepage relevance
Authors: Deepak Agarwal, Shaunak Chatterjee, Yang Yang, Liang Zhang
Venue: Conference WWW 2015
Year: 2015

This paper considers an application of showing promotional widgets to web users on the homepage of a major professional social network site. The types of widgets include address book invitation, group join, friends' skill endorsement and so forth. The objective is to optimize user engagement under certain business constraints. User actions on each widget may have very different downstream utilities, and quantification of such utilities can sometimes be quite difficult. Since there are multiple widgets to rank when a user visits, launching a personalized model to simply optimize user engagement such as clicks is often inappropriate. In this paper we propose a scalable constrained optimization framework to solve this problem. We consider several different types of constraints according to the business needs for this application. We show through both offline experiments and online A/B tests that our optimization framework can lead to significant improvement in user engagement while satisfying the desired set of business objectives.
Activity Ranking in LinkedIn Feed
Authors: Deepak Agarwal, Bee-Chung Chen, Rupesh Gupta, Joshua Hartman, Qi He, Anand Iyer, Sumanth Kolar, Yiming Ma, Pannagadatta Shivaswamy, Ajit Singh, Liang Zhang
Venue: Conference KDD 2014
Year: 2014

Users on an online social network site generate a large number of heterogeneous activities, ranging from connecting with other users, to sharing content, to updating their profiles. The set of activities within a user's network neighborhood forms a stream of updates for the user's consumption. In this paper, we report our experience with the problem of ranking activities in the LinkedIn homepage feed. In particular, we provide a taxonomy of social network activities, describe a system architecture (with a number of key components open-sourced) that supports fast iteration in model development, demonstrate a number of key factors for effective ranking, and report experimental results from extensive online bucket tests.
Near Real-time Optimization of Activity-based Notifications
Authors: Yan Gao, Viral Gupta, Jinyun Yan, Changji Shi, Zhongen Tao, P. J. Xiao, Curtis Wang, Shipeng Yu, Rómer Rosales, Ajith Muralidharan, Shaunak Chatterjee
Venue: Conference KDD 2018
Year: 2018

In recent years, social media applications (e.g., Facebook, LinkedIn) have created mobile applications (apps) to give their members instant and real-time access from anywhere. To keep members informed and drive timely engagement, these mobile apps send event notifications. However, sending notifications for every possible event would result in too many notifications which would in turn annoy members and create a poor member experience. In this paper, we present our strategy of optimizing notifications to balance various utilities (e.g., engagement, send volume) by formulating the problem using constrained optimization. To guarantee freshness of notifications, we implement the solution in a stream computing system in which we make multi-channel send decisions in near real-time. Through online A/B test results, we show the effectiveness of our proposed approach on tens of millions of members.
A State Transition Model for Mobile Notifications via Survival Analysis
Authors: Yiping Yuan, Jing Zhang, Shaunak Chatterjee, Shipeng Yu, Rómer Rosales
Venue: Conference WSDM 2019
Year: 2019

Mobile notifications have become a major communication channel for social networking services to keep users informed and engaged. As more mobile applications push notifications to users, they constantly face decisions on what to send, when and how. A lack of research and methodology commonly leads to heuristic decision making. Many notifications arrive at an inappropriate moment or introduce too many interruptions, failing to provide value to users and spurring users' complaints. In this paper we explore unique features of interactions between mobile notifications and user engagement. We propose a state transition framework to quantitatively evaluate the effectiveness of notifications. Within this framework, we develop a survival model for badging notifications assuming a log-linear structure and a Weibull distribution. Our results show that this model achieves more flexibility for applications and superior prediction accuracy than a logistic regression model. In particular, we provide an online use case on notification delivery time optimization to show how we make better decisions, drive more user engagement, and provide more value to users.
Topic Structure-Aware Neural Language Model: Unified language model that maintains word and topic ordering by their embedded representations
Authors: Noriaki Kawamae
Venue: Conference WWW 2019
Year: 2019

Our goal is to exploit a unified language model so as to explain the generative process of documents precisely in view of their semantic and topic structures. Because various methods model documents in disparate ways, we are motivated by the expectation that coordinating these methods will allow us to achieve this goal more efficiently than using them in isolation; we combine topic models, embedding models, and neural language models. As we focus on the fact that topic models can be shared among, and indeed complement embedding models and neural language models, we propose Word and topic 2 vec (Wat2vec), and Topic Structure-Aware Neural Language Model (TSANL). Wat2vec uses topics as global semantic information and local semantic information as embedding representations of topics and words, and embeds both words and topics in the same space. TSANL uses recurrent neural networks to capture long-range dependencies over topics and words. Since existing topic models demand time consuming learning and have poor scalability, both due to breaking the document?s structure such as order of words and topics, TSANL maintains the orders of words and topics as phrases and segments, respectively. TSANL reduces the calculation cost and required memory by feeding topic recurrent neural networks, and topic specific word networks with these embedding representations. Experiments show that TSANL maintains both segments and topical phrases, and so enhances previous models.
Eigenspace-based anomaly detection in computer systems
Authors: Tsuyoshi Idé, Hisashi Kashima
Venue: Conference KDD 2004
Year: 2004

We report on an automated runtime anomaly detection method at the application layer of multi-node computer systems. Although several network management systems are available in the market, none of them have sufficient capabilities to detect faults in multi-tier Web-based systems with redundancy. We model a Web-based system as a weighted graph, where each node represents a "service" and each edge represents a dependency between services. Since the edge weights vary greatly over time, the problem we address is that of anomaly detection from a time sequence of graphs.In our method, we first extract a feature vector from the adjacency matrix that represents the activities of all of the services. The heart of our method is to use the principal eigenvector of the eigenclusters of the graph. Then we derive a probability distribution for an anomaly measure defined for a time-series of directional data derived from the graph sequence. Given a critical probability, the threshold value is adaptively updated using a novel online algorithm.We demonstrate that a fault in a Web application can be automatically detected and the faulty services are identified without using detailed knowledge of the behavior of the system.
Learning dynamic context graphs for predicting social events
Authors: Songgaojun Deng, Huzefa Rangwala, Yue Ning
Venue: Conference KDD 2019
Year: 2019

Event forecasting with an aim at modeling contextual information is an important task for applications such as automated analysis generation and resource allocation. Captured contextual information for an event of interest can aid human analysts in understanding the factors associated with that event. However, capturing contextual information within event forecasting is challenging due to several factors: (i) uncertainty of context structure and formulation, (ii) high dimensional features, and (iii) adaptation of features over time. Recently, graph representations have demonstrated success in applications such as traffic forecasting, social influence prediction, and visual question answering systems. In this paper, we study graph representations in modeling social events to identify dynamic properties of event contexts as social indicators. Inspired by graph neural networks, we propose a novel graph convolutional network for predicting future events (e.g., civil unrest movements). We extract and learn graph representations from historical/prior event documents. By employing the hidden word graph features, our proposed model predicts the occurrence of future events and identifies sequences of dynamic graphs as event context. Experimental results on multiple real-world data sets show that the proposed method is competitive against various state-of-the-art methods for social event prediction.
Data-driven Automatic Treatment Regimen Development and Recommendation
Authors: Leilei Sun, Chuanren Liu, Chonghui Guo, Hui Xiong, Yanming Xie
Venue: Conference KDD 2016
Year: 2016

The analysis of large-scale Electrical Medical Records (EMRs) has the potential to develop and optimize clinical treatment regimens. A treatment regimen usually includes a series of doctor orders containing rich temporal and heterogeneous information. However, in many existing studies, a doctor order is simplified as an event code and a treatment record is simplified as a code sequence. Thus, the information inherent in doctor orders is not fully used for in-depth analysis. In this paper, we aim at exploiting the rich information in doctor orders and developing data-driven approaches for improving clinical treatments. To this end, we first propose a novel method to measure the similarities between treatment records with consideration of sequential and multifaceted information in doctor orders. Then, we propose an efficient density-based clustering algorithm to summarize large-scale treatment records, and extract a semantic representation of each treatment cluster. Finally, we develop a unified framework to evaluate the discovered treatment regimens, and find the most effective treatment regimen for new patients. In the empirical study, we validate our methods with EMRs of 27,678 patients from 14 hospitals. The results show that: 1) Our method can successfully extract typical treatment regimens from large-scale treatment records. The extracted treatment regimens are intuitive and provide managerial implications for treatment regimen design and optimization. 2) By recommending the most effective treatment regimens, the total cure rate in our data improves from 19.89% to 21.28%, and the effective rate increases up to 98.29%.
Mining the search trails of surfing crowds: identifying relevant websites from user activity
Authors: Mikhail Bilenko, Ryen W. White
Venue: Conference WWW 2008
Year: 2008

The paper proposes identifying relevant information sources from the history of combined searching and browsing behavior of many Web users. While it has been previously shown that user interactions with search engines can be employed to improve document ranking, browsing behavior that occurs beyond search result pages has been largely overlooked in prior work. The paper demonstrates that users' post-search browsing activity strongly reflects implicit endorsement of visited pages, which allows estimating topical relevance of Web resources by mining large-scale datasets of search trails. We present heuristic and probabilistic algorithms that rely on such datasets for suggesting authoritative websites for search queries. Experimental evaluation shows that exploiting complete post-search browsing trails outperforms alternatives in isolation (e.g., clickthrough logs), and yields accuracy improvements when employed as a feature in learning to rank for Web search.
Evaluation by comparing result sets in context
Authors: Paul Thomas, David Hawking
Venue: Conference CIKM 2006
Year: 2006

Familiar evaluation methodologies for information retrieval (IR) are not well suited to the task of comparing systems in many real settings. These systems and evaluation methods must support contextual, interactive retrieval over changing, heterogeneous data collections, including private and confidential information.We have implemented a comparison tool which can be inserted into the natural IR process. It provides a familiar search interface, presents a small number of result sets in side-by-side panels, elicits searcher judgments, and logs interaction events. The tool permits study of real information needs as they occur, uses the documents actually available at the time of the search, and records judgments taking into account the instantaneous needs of the searcher.We have validated our proposed evaluation approach and explored potential biases by comparing different whole-of-Web search facilities using a Web-based version of the tool. In four experiments, one with supplied queries in the laboratory and three with real queries in the workplace, subjects showed no discernable left-right bias and were able to reliably distinguish between high- and low-quality result sets. We found that judgments were strongly predicted by simple implicit measures.Following validation we undertook a case study comparing two leading whole-of-Web search engines. The approach is now being used in several ongoing investigations.
Studying the Use of Popular Destinations to Enhance Web Search Interaction
Authors: Ryen W. White, Mikhail Bilenko, Silviu Cucerzan
Venue: Conference SIGIR 2007
Year: 2007

We present a novel Web search interaction feature which, for a given query, provides links to websites frequently visited by other users with similar information needs. These popular destinations complement traditional search results, allowing direct navigation to authoritative resources for the query topic. Destinations are identified using the history of search and browsing behavior of many users over an extended time period, whose collective behavior provides a basis for computing source authority. We describe a user study which compared the suggestion of destinations with the previously proposed suggestion of related queries, as well as with traditional, unaided Web search. Results show that search enhanced by destination suggestions outperforms other systems for exploratory tasks, with best performance obtained from mining past user behavior at query-level granularity.
Bayesian Models for Product Size Recommendations
Authors: Vivek Sembium, Rajeev Rastogi, Lavanya Sita Tekumalla, Atul Saroop
Venue: Conference WWW 2018
Year: 2018

Lack of calibrated product sizing in popular categories such as apparel and shoes leads to customers purchasing incorrect sizes, which in turn results in high return rates due to fit issues. We address the problem of product size recommendations based on customer purchase and return data. We propose a novel approach based on Bayesian logit and probit regression models with ordinal categories Small, Fit, Largeto model size fits as a function of the difference between latent sizes of customers and products. We propose posterior computation based on mean-field variational inference, leveraging the Polya-Gamma augmentation for the logit prior, that results in simple updates, enabling our technique to efficiently handle large datasets. Our Bayesian approach effectively deals with issues arising from noise and sparsity in the data providing robust recommendations. Offline experiments with real-life shoe datasets show that our model outperforms the state-of-the-art in 5 of 6 datasets. and leads to an improvement of 17-26% in AUC over baselines when predicting size fit outcomes.
Panther: Fast top-k similarity search on large networks
Authors: Jing Zhang, Jie Tang, Cong Ma, Hanghang Tong, Yu Jing, Juanzi Li
Venue: Conference KDD 2015
Year: 2015

Estimating similarity between vertices is a fundamental issue in network analysis across various domains, such as social networks and biological networks. Methods based on common neighbors and structural contexts have received much attention. However, both categories of methods are difficult to scale up to handle large networks (with billions of nodes). In this paper, we propose a sampling method that provably and accurately estimates the similarity between vertices. The algorithm is based on a novel idea of random path. Specifically, given a network, we perform R random walks, each starting from a randomly picked vertex and walking T steps. Theoretically, the algorithm guarantees that the sampling size R = O(2ε-2 log2 T) depends on the error-bound ε, the confidence level (1 -- δ), and the path length T of each random walk.