OpenRaiser/Intern-Atlas huggingface dataset:
Personalized trip recommendation with poi availability and uncertain traveling time
Authors: Chenyi Zhang, Hongwei Liang, Ke Wang, Jianling Sun
Venue: Conference CIKM 2015
Year: 2015

As location-based social network (LBSN) services become increasingly popular, trip recommendation that recommends a sequence of points of interest (POIs) to visit for a user emerges as one of many important applications of LBSNs. Personalized trip recommendation tailors to users' specific tastes by learning from past check-in behaviors of users and their peers. Finding the optimal trip that maximizes user's experiences for a given time budget constraint is an NP hard problem and previous solutions do not consider two practical and important constraints. One constraint is POI availability where a POI may be only available during a certain time window. Another constraint is uncertain traveling time where the traveling time between two POIs is uncertain. This work presents efficient solutions to personalized trip recommendation by incorporating these constraints to prune the search space. We evaluated the efficiency and effectiveness of our solutions on real life LBSN data sets.
Tensor rank estimation and completion via cp-based nuclear norm
Authors: Qiquan Shi, Haiping Lu, Yiu-ming Cheung
Venue: Conference CIKM 2017
Year: 2017

Tensor completion (TC) is a challenging problem of recovering missing entries of a tensor from its partial observation. One main TC approach is based on CP/Tucker decomposition. However, this approach often requires the determination of a tensor rank a priori. This rank estimation problem is difficult in practice. Several Bayesian solutions have been proposed but they often under/over-estimate the tensor rank while being quite slow. To address this problem of rank estimation with missing entries, we view the weight vector of the orthogonal CP decomposition of a tensor to be analogous to the vector of singular values of a matrix. Subsequently, we define a new CP-based tensor nuclear norm as the $L_1$-norm of this weight vector. We then propose Tensor Rank Estimation based on $L_1$-regularized orthogonal CP decomposition (TREL1) for both CP-rank and Tucker-rank. Specifically, we incorporate a regularization with CP-based tensor nuclear norm when minimizing the reconstruction error in TC to automatically determine the rank of an incomplete tensor. Experimental results on both synthetic and real data show that: 1) Given sufficient observed entries, TREL1 can estimate the true rank (both CP-rank and Tucker-rank) of incomplete tensors well; 2) The rank estimated by TREL1 can consistently improve recovery accuracy of decomposition-based TC methods; 3) TREL1 is not sensitive to its parameters in general and more efficient than existing rank estimation methods.
Online outlier exploration over large datasets
Authors: Lei Cao, Mingrui Wei, Di Yang, Elke A. Rundensteiner
Venue: Conference KDD 2015
Year: 2015

Traditional outlier detection systems process each individual outlier detection request instantiated with a particular parameter setting one at a time. This is not only prohibitively time-consuming for large datasets, but also tedious for analysts as they explore the data to hone in on the appropriate parameter setting and desired results.
Large-scale user visits understanding and forecasting with deep spatial-temporal tensor factorization framework
Authors: Xiaoyang Ma, Lan Zhang, Lan Xu, Zhicheng Liu, Ge Chen, Zhili Xiao, Yang Wang, Zhengtao Wu
Venue: Conference KDD 2019
Year: 2019

Understanding and forecasting user visits is of great importance for a variety of tasks, e.g., online advertising, which is one of the most profitable business models for Internet services. Publishers sell advertising spaces in advance with user visit volume and attributes guarantees. There are usually tens of thousands of attribute combinations in an online advertising system. The key problem is how to accurately forecast the number of user visits for each attribute combination. Many traditional work characterizing temporal trends of every single time series are quite inefficient for large-scale time series. Recently, a number of models based on deep learning or matrix factorization have been proposed for high-dimensional time series forecasting. However, most of them neglect correlations among attribute combinations, or are tailored for specific applications, resulting in poor adaptability for different business scenarios.Besides, sophisticated deep learning models usually cause high time and space complexity. There is still a lack of an efficient highly scalable and adaptable solution for accurate high-dimensional time series forecasting. To address this issue, in this work, we conduct a thorough analysis on large-scale user visits data and propose a novel deep spatial-temporal tensor factorization framework, which provides a general design for high-dimensional time series forecasting. We deployed the proposed framework in Tencent online guaranteed delivery advertising system, and extensively evaluated the effectiveness and efficiency of the framework in two different large-scale application scenarios. The results show that our framework outperforms existing methods in prediction accuracy. Meanwhile, it significantly reduces the parameter number and is resistant to incomplete data with up to 20% missing values.
Retrieve-and-read: Multi-task learning of information retrieval and reading comprehension
Authors: Kyosuke Nishida, Itsumi Saito, Atsushi Otsuka, Hisako Asano, Junji Tomita
Venue: Conference CIKM 2018
Year: 2018

This study considers the task of machine reading at scale (MRS) wherein, given a question, a system first performs the information retrieval (IR) task of finding relevant passages in a knowledge source and then carries out the reading comprehension (RC) task of extracting an answer span from the passages. Previous MRS studies, in which the IR component was trained without considering answer spans, struggled to accurately find a small number of relevant passages from a large set of passages. In this paper, we propose a simple and effective approach that incorporates the IR and RC tasks by using supervised multi-task learning in order that the IR component can be trained by considering answer spans. Experimental results on the standard benchmark, answering SQuAD questions using the full Wikipedia as the knowledge source, showed that our model achieved state-of-the-art performance. Moreover, we thoroughly evaluated the individual contributions of our model components with our new Japanese dataset and SQuAD. The results showed significant improvements in the IR task and provided a new perspective on IR for RC: it is effective to teach which part of the passage answers the question rather than to give only a relevance score to the whole passage.
Modeling dynamic competition on crowdfunding markets
Authors: Yusan Lin, Peifeng Yin, Wang-Chien Lee
Venue: Conference WWW 2018
Year: 2018

The often fierce competition on crowdfunding markets can significantly affect project success. While various factors have been considered in predicting the success of crowdfunding projects, to the best knowledge of the authors, the phenomenon of competition has not been investigated. In this paper, we study the competition on crowdfunding markets through data analysis, and propose a probabilistic generative model, Dynamic Market Competition (DMC) model, to capture the competitiveness of projects in crowdfunding. Through an empirical evaluation using the pledging history of past crowdfunding projects, our approach has shown to capture the competitiveness of projects very well, and significantly outperforms several baseline approaches in predicting the daily collected funds of crowdfunding projects, reducing errors by 31.73% to 45.14%. In addition, our analyses on the correlations between project competitiveness, project design factors, and project success indicate that highly competitive projects, while being winners under various setting of project design factors, are particularly impressive with high pledging goals and high price rewards, comparing to medium and low competitive projects. Finally, the competitiveness of projects learned by DMC is shown to be very useful in applications of predicting final success and days taken to hit pledging goal, reaching 85% accuracy and error of less than 7 days, respectively, with limited information at early pledging stage.
Entropy-based criterion in categorical clustering
Authors: Tao Li 0001, Sheng Ma, Mitsunori Ogihara
Venue: Conference ICML 2004
Year: 2004

Entropy-type measures for the heterogeneity of clusters have been used for a long time. This paper studies the entropy-based criterion in clustering categorical data. It first shows that the entropy-based criterion can be derived in the formal framework of probabilistic clustering models and establishes the connection between the criterion and the approach based on dissimilarity co-efficients. An iterative Monte-Carlo procedure is then presented to search for the partitions minimizing the criterion. Experiments are conducted to show the effectiveness of the proposed procedure.
Graph Attention Networks over Edge Content-Based Channels
Authors: Lu Lin 0001, Hongning Wang
Venue: Conference KDD 2020
Year: 2020

Edges play a crucial role in passing information on a graph, especially when they carry textual content reflecting semantics behind how nodes are linked and interacting with each other. In this paper, we propose a channel-aware attention mechanism enabled by edge text content when aggregating information from neighboring nodes; and we realize this mechanism in a graph autoencoder framework. Edge text content is encoded as low-dimensional mixtures of latent topics, which serve as semantic channels for topic-level information passing on edges. We embed nodes and topics in the same latent space to capture their mutual dependency when decoding the structural and textual information on graph. We evaluated the proposed model on Yelp user-item bipartite graph and StackOverflow user-user interaction graph. The proposed model outperformed a set of baselines on link prediction and content prediction tasks. Qualitative evaluations also demonstrated the descriptive power of the learnt node embeddings, showing its potential as an interpretable representation of graphs.
Dynamic Local Models for Online Recommendation
Authors: Marie Al-Ghossein, Talel Abdessalem, Anthony Barré
Venue: Conference WWW 2018
Year: 2018

With the explosion of the volume of user-generated data, designing online recommender systems that learn from data streams has become essential. These systems rely on incremental learning that continuously update models as new observations arrive and they should be able to adapt to drifts in real-time. User preferences evolve over time and tracking their evolution is not an easy task. In addition to the low number of observations available per user, the preferences change at different moments and in different ways for each individual. In this paper, we propose a novel approach based on local models to address this problem. Local models are known for their ability to capture diverse preferences among user subsets. Our approach automatically detects the drift of preferences that leads a user to adopt a behavior closer to the users of another subset, and adjusts the models accordingly. Our experiments on real world datasets show promising results and prove the effectiveness of using local models to adapt to changes in user preferences.
Strategic system comparisons via targeted relevance judgments
Authors: Alistair Moffat, William Webber, Justin Zobel
Venue: Conference SIGIR 2007
Year: 2007

Relevance judgments are used to compare text retrieval systems. Given a collection of documents and queries, and a set of systems being compared, a standard approach to forming judgments is to manually examine all documents that are highly ranked by any of the systems. However, not all of these relevance judgments provide the same benefit to the final result, particularly if the aim is to identify which systems are best, rather than to fully order them. In this paper we propose new experimental methodologies that can significantly reduce the volume of judgments required in system comparisons. Using rank-biased precision, a recently proposed effectiveness measure, we show that judging around 200 documents for each of 50 queries in a TREC-scale system evaluation containing over 100 runs is sufficient to identify the best systems.
On building fair and reusable test collections using bandit techniques
Authors: Ellen M. Voorhees
Venue: Conference CIKM 2018
Year: 2018

While test collections are a vital piece of the research infrastructure for information retrieval, constructing fair, reusable test collections for large data sets is challenging because of the number of human relevance assessments required. Various approaches for minimizing the number of judgments required have been proposed including a suite of methods based on multi-arm bandit optimization techniques. However, most of these approaches look to maximize the total number of relevant documents found, which is not necessarily fair, and they have only been demonstrated in simulation on existing test collections. The TREC 2017 Common Core track provided the opportunity to build a collection de novo using a bandit method. Doing so required addressing two problems not encountered in simulation: giving the human judges time to learn a topic and allocating the overall judgment budget across topics. The resulting modified bandit technique was used to build the 2017 Common Core test collection consisting of approximately 1.8 million news articles, 50 topics, and 30,030 judgments. Unfortunately, the constructed collection is of lower quality than anticipated: a large percentage of the known relevant documents were retrieved by only one team, and for 21 topics, more than a third of the judged documents are relevant. As such the collection is less reusable than desired. Further analysis demonstrates that the greedy approach common to most bandit methods can be unfair even to the runs participating in the collection-building process when the judgment budget is small relative to the (unknown) number of relevant documents.
Adversarial point-of-interest recommendation
Authors: Fan Zhou, Ruiyang Yin, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, Jin Wu
Venue: Conference WWW 2019
Year: 2019

Point-of-interest (POI) recommendation is essential to a variety of services for both users and business. An extensive number of models have been developed to improve the recommendation performance by exploiting various characteristics and relations among POIs (e.g., spatio-temporal, social, etc.). However, very few studies closely look into the underlying mechanism accounting for why users prefer certain POIs to others. In this work, we initiate the first attempt to learn the distribution of user latent preference by proposing an Adversarial POI Recommendation (APOIR) model, consisting of two major components: (1) the recommender (R) which suggests POIs based on the learned distribution by maximizing the probabilities that these POIs are predicted as unvisited and potentially interested; and (2) the discriminator (D) which distinguishes the recommended POIs from the true check-ins and provides gradients as the guidance to improve R in a rewarding framework. Two components are co-trained by playing a minimax game towards improving itself while pushing the other to the boundary. By further integrating geographical and social relations among POIs into the reward function as well as optimizing R in a reinforcement learning manner, APOIR obtains significant performance improvement in four standard metrics compared to the state of the art methods.
Learning to generate rock descriptions from multivariate well logs with hierarchical attention
Authors: Bin Tong, Martin Klinkigt, Makoto Iwayama, Toshihiko Yanase, Yoshiyuki Kobayashi, Anshuman Sahu, Ravigopal Vennelakanti
Venue: Conference KDD 2017
Year: 2017

In the shale oil & gas industry, operators are looking toward big data analytics to optimize operations and reduce cost. In this paper, we mainly focus on how to assist operators in understanding the subsurface formation, thereby helping them make optimal decisions. A large number of geology reports and well logs describing the sub-surface have been accumulated over years. Issuing geology reports is more time consuming and depends more on the expertise of engineers than acquiring the well logs. To assist in issuing geology reports, we propose an encoder-decoder-based model to automatically generate rock descriptions in human-readable format from multivariate well logs. Due to the different formats of data, this task differs dramatically from image and video captioning. The challenges are how to model structured rock descriptions and leverage the information in multivariate well logs. To achieve this, we design a hierarchical structure and two forms of attention for the decoder. Extensive validations are conducted on public well data of North Dakota in the United States. We show that our model is effective in generating rock descriptions. The two forms of attention enable the provision of a better insight into relations between well-log types and rock properties with our model from a data-driven perspective.
Re-Ranking Answer Selection with Similarity Aggregation
Authors: Dong Chen 0013, Shaoliang Peng, Kenli Li 0001, Ying Xu, Jinling Zhang, Xiaolan Xie
Venue: Conference SIGIR 2020
Year: 2020

Answer selection plays a crucial role in natural language processing. and thus has received much attention. Many recent works treat it as an ad-hoc retrieval problem where ranking optimization accounts for a large proportion. Previous works mainly consider the similarity between answer and question, but rarely utilize similarity and dissimilarity relationship in the answers candidate set. In this paper, we propose a similarity aggregation method to rerank the results produced by different baseline neural networks. The key idea of similarity aggregation is that true matches should not only similar to other true matches, but also dissimilar with false matches, and inspired by multi-view verification, the true answers should have the same ranking to the question in different baseline methods and false answers are the same. The empirical results, from the public benchmark task of answer selection, demonstrate that our method has significant improvement over the baseline methods.
ST- SiameseNet: Spatio-Temporal Siamese Networks for Human Mobility Signature Identification
Authors: Huimin Ren, Menghai Pan, Yanhua Li, Xun Zhou 0001, Jun Luo 0007
Venue: Conference KDD 2020
Year: 2020

Given the historical movement trajectories of a set of individual human agents (e.g., pedestrians, taxi drivers) and a set of new trajectories claimed to be generated by a specific agent, the Human Mobility Signature Identification (HuMID) problem aims at validating if the incoming trajectories were indeed generated by the claimed agent. This problem is important in many real-world applications such as driver verification in ride-sharing services, risk analysis for auto insurance companies, and criminal identification. Prior work on identifying human mobility behaviors requires additional data from other sources besides the trajectories, e.g., sensor readings in the vehicle for driving behavior identification. However, these data might not be universally available and is costly to obtain. To deal with this challenge, in this work, we make the first attempt to match identities of human agents only from the observed location trajectory data by proposing a novel and efficient framework named Spatio-temporal Siamese Networks (ST-SiameseNet). For each human agent, we extract a set of profile and online features from his/her trajectories. We train ST-SiameseNet to predict the mobility signature similarity between each pair of agents, where each agent is represented by his/her trajectories and the extracted features. Experimental results on a real-world taxi trajectory dataset show that our proposed ST-SiamesNet can achieve an $F_1$ score of $0.8508$, which significantly outperforms the state-of-the-art techniques.
Graph Similarity Description: How Are These Graphs Similar?
Authors: Corinna Coupette, Jilles Vreeken
Venue: Conference KDD 2021
Year: 2021

How do social networks differ across platforms? How do information networks change over time? Answering questions like these requires us to compare two or more graphs. This task is commonly treated as a measurement problem, but numerical answers give limited insight. Here, we argue that if the goal is to gain understanding, we should treat graph similarity assessment as a description problem instead. We formalize this problem as a model selection task using the Minimum Description Length principle, capturing the similarity of the input graphs in a common model and the differences between them in transformations to individual models. To discover good models, we propose Momo, which breaks the problem into two parts and introduces efficient algorithms for each. Through an extensive set of experiments on a wide range of synthetic and real-world graphs, we confirm that Momo works well in practice.
LoOP: local outlier probabilities
Authors: Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
Venue: Conference CIKM 2009
Year: 2009

Many outlier detection methods do not merely provide the decision for a single data object being or not being an outlier but give also an outlier score or "outlier factor" signaling "how much" the respective data object is an outlier. A major problem for any user not very acquainted with the outlier detection method in question is how to interpret this "factor" in order to decide for the numeric score again whether or not the data object indeed is an outlier. Here, we formulate a local density based outlier detection method providing an outlier "score" in the range of [0, 1] that is directly interpretable as a probability of a data object for being an outlier.
Machine Learning Explainability and Robustness: Connected at the Hip
Authors: Anupam Datta, Matt Fredrikson, Klas Leino, Kaiji Lu, Shayak Sen, Zifan Wang
Venue: Conference KDD 2021
Year: 2021

This tutorial examines the synergistic relationship between explainability methods for machine learning and a significant problem related to model quality: robustness against adversarial perturbations. We begin with a broad overview of approaches to explainable AI, before narrowing our focus to post-hoc explanation methods for predictive models. We discuss perspectives on what constitutes a "good'' explanation in various settings, with an emphasis on axiomatic justifications for various explanation methods. In doing so, we will highlight the importance of an explanation method's faithfulness to the target model, as this property allows one to distinguish between explanations that are unintelligible because of the method used to produce them, and cases where a seemingly poor explanation points to model quality issues. Next, we introduce concepts surrounding adversarial robustness, including adversarial attacks as well as a range of corresponding state-of-the-art defenses. Finally, building on the knowledge presented thus far, we present key insights from the recent literature on the connections between explainability and robustness, showing that many commonly-perceived explainability issues may be caused by non-robust model behavior. Accordingly, a careful study of adversarial examples and robustness can lead to models whose explanations better appeal to human intuition and domain knowledge.