OpenRaiser/Intern-Atlas huggingface dataset:
Sample Optimization For Display Advertising
Authors: Hongliang Fei, Shulong Tan, Pengju Guo, Wenbo Zhang, Hongfang Zhang, Ping Li
Venue: CIKM
Year: 2020

Sample optimization, which involves sample augmentation and sample refinement, is an essential but often neglected component in modern display advertising platforms. Due to the massive number of ad candidates, industrial ad service usually leverages a multi-layer funnel-shaped structure involving at least two stages: candidate generation and re-ranking. In the candidate generation step, an offline neural network matching model is often trained based on past click/conversion data to obtain the user feature vector and ad feature vector. However, there is a covariate shift problem between the user observed ads and all possible ones. As a result, the candidate generation model trained from the click/conversion history cannot fully capture users' potential intentions or generalize well to unseen ads. In this paper, we utilize several sample optimization strategies to alleviate the covariate shift problem for training candidate generation models. We have launched these strategies in Baidu display ad platform and achieved considerable improvements in offline metrics, including both offline click-recall, cost-recall, as well as online metric cost per mille (CPM).
A Reinforced Semi-supervised Neural Network for Helpful Review Identification
Authors: Yue Feng, Miao Fan, Mingming Sun, Ping Li
Venue: CIKM
Year: 2020

It is crucial to recommend helpful product reviews to consumers in e-commercial service, as the helpful ones can promote consumption. Existing methods for identifying helpful reviews are based on the supervised learning paradigm. The capacity of supervised methods, however, is limited by the lack of annotated reviews. In addition, there is a serious distributional bias between the labeled and unlabeled reviews. Therefore, this paper proposes a reinforced semi-supervised neural learning method (abbreviated as RSSNL) for helpful review identification, which can automatically select high-related unlabeled reviews to help training. Concretely, RSSNL composes with a reinforced unlabeled review selection policy and a semi-supervised pseudo-labeling review classifier. These two parts train jointly and integrate together based on the policy gradient framework. Extensive experiments on Amazon product reviews verify the effectiveness of RSSNL for using unlabeled reviews.
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction
Authors: Yufei Feng, Fuyu Lv, Binbin Hu, Fei Sun, Kun Kuang, Yang Liu, Qingwen Liu, Wenwu Ou
Venue: CIKM
Year: 2020

Click-through rate (CTR) prediction is a critical task for many industrial systems, such as display advertising and recommender systems. Recently, modeling user behavior sequences attracts much attention and shows great improvements in the CTR field. Existing works mainly exploit attention mechanism based on embedding product when considering relations between user behaviors and target item. However, this methodology lacks of concrete semantics and overlooks the underlying reasons driving a user to click on a target item. In this paper, we propose a new framework named Multiplex Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex relations between user behaviors and target item to enhance CTR prediction. Multiplex relations consist of meaningful semantics, which can bring a better understanding on users' interests from different perspectives. To explore and model multiplex relations, we propose to incorporate various graphs (e.g., knowledge graph and item-item similarity graph) to construct multiple relational paths between user behaviors and target item. Then Bi-LSTM is applied to encode each path in the path extractor layer. A path fusion network and a path activation network are devised to adaptively aggregate and finally learn the representation of all paths for CTR prediction. Extensive offline and online experiments clearly verify the effectiveness of our framework.
RelSen: An Optimization-based Framework for Simultaneously Sensor Reliability Monitoring and Data Cleaning
Authors: Cheng Feng, Xiao Liang, Daniel Schneegass, Pengwei Tian
Venue: CIKM
Year: 2020

Recent advances in the Internet of Things (IoT) technology have led to a surge on the popularity of sensing applications. As a result, people increasingly rely on information obtained from sensors to make decisions in their daily life. Unfortunately, in most sensing applications, sensors are known to be error-prone and their measurements can become misleading at any unexpected time. Therefore, in order to enhance the reliability of sensing applications, apart from the physical phenomena/processes of interest, we believe it is also highly important to monitor the reliability of sensors and clean the sensor data before analysis on them being conducted. Existing studies often regard sensor reliability monitoring and sensor data cleaning as separate problems. In this work, we propose RelSen, a novel optimization-based framework to address the two problems simultaneously via utilizing the mutual dependence between them. Furthermore, RelSen is not application-specific as its implementation assumes a minimal prior knowledge of the process dynamics under monitoring. This significantly improves its generality and applicability in practice. In our experiments, we apply RelSen on an outdoor air pollution monitoring system and a condition monitoring system for a cement rotary kiln. Experimental results show that our framework can timely identify unreliable sensors and remove sensor measurement errors caused by three types of most commonly observed sensor faults.
Fine-Tuned Compressed Representations of Vessel Trajectories
Authors: Giannis Fikioris, Kostas Patroumpas, Alexander Artikis, Georgios Paliouras, Manolis Pitsikalis
Venue: CIKM
Year: 2020

In the maritime domain, vessels typically maintain straight, predictable routes at open sea, except in the rare cases of adverse weather conditions, accidents and traffic restrictions. Consequently, large amounts of streaming positional updates from vessels can hardly contribute additional knowledge about their actual motion patterns. We have been developing a system for vessel trajectory compression discarding a significant part of the original positional updates, with minimal trajectory reconstruction error. In this work, we present an extension of this system, that allows the user to fine-tune trajectory compression according to the requirements of a given application. The extended system avoids the issues of hyper-parameter tuning, supports incremental optimization and facilitates composite maritime event recognition. Finally, we report empirical results from a comprehensive empirical evaluation against two real-world datasets of vessel positions.
Query-to-Session Matching: Do NOT Forget History and Future during Response Selection for Multi-Turn Dialogue Systems
Authors: Zhenxin Fu, Shaobo Cui, Feng Ji, Ji Zhang, Haiqing Chen, Dongyan Zhao, Rui Yan
Venue: CIKM
Year: 2020

Given a user query, traditional multi-turn retrieval-based dialogue systems first retrieve a set of candidate responses from the historical dialogue sessions. Then the response selection models select the most appropriate response to the given query. However, previous work only considers the matching between the query and the response but ignores the informative dialogue session in which the response is located. Nevertheless, this session, composed of the response, the response's history and the response's future, always contains valuable contextual information which can help the response selection task. More specifically, if the current query and a response's history both refer to the same question, we can conclude that this response is quite likely to answer this query. As for the response's future, it can always provide contextual hints and supplementary information that might be omitted in the response. Inspired by such motivation, we propose a query-to-session matching (QSM) framework to make full use of the session information: matching the query with the candidate session instead of the response only. Different from the previous work which ranks response directly, the response in the session with the highest query-to-session matching score will be selected as the desired response. In our proposed framework, the query, history, and future are all sequences of utterances, which makes it necessary to model the relationships among the utterances. So we propose a novel dialogue flow aware query-to-session matching (DF-QSM) model. The dialogue flows model the relationships among the utterances through a memory network. To our best knowledge, our paper is the first work to utilize both the response's history and future in the response selection task. The experimental results on three multi-turn response selection benchmarks show that our proposed model outperforms existing state-of-the-art methods by a large margin.
A View-Adversarial Framework for Multi-View Network Embedding
Authors: Dongqi Fu, Zhe Xu, Bo Li, Hanghang Tong, Jingrui He
Venue: CIKM
Year: 2020

Network embedding has demonstrated effective empirical performance for various network mining tasks such as node classification, link prediction, clustering, and anomaly detection. However, most of these algorithms focus on the single-view network scenario. From a real-world perspective, one individual node can have different connectivity patterns in different networks. For example, one user can have different relationships on Twitter, Facebook, and LinkedIn due to varying user behaviors on different platforms. In this case, jointly considering the structural information from multiple platforms (i.e., multiple views) can potentially lead to more comprehensive node representations, and eliminate noises and bias from a single view. In this paper, we propose a view-adversarial framework to generate comprehensive and robust multi-view network representations named VANE, which is based on two adversarial games. The first adversarial game enhances the comprehensiveness of the node representation by discriminating the view information which is obtained from the subgraph induced by neighbors of that node. The second adversarial game improves the robustness of the node representation with the challenging of fake node representations from the generative adversarial net. We conduct extensive experiments on downstream tasks with real-world multi-view networks, which shows that our proposed VANE framework significantly outperforms other baseline methods.
Semantic Search over Structured Data
Authors: Sainyam Galhotra, Udayan Khurana
Venue: CIKM
Year: 2020

Searching through large structured data is a crucial task in enterprise as well as online search. Existing keyword-based search methods were mostly designed for web document search, and support a limited variety of query types suitable for structured data. Their main underlying drawback is the lack of semantic understanding of the data and users' natural language queries, resulting in a fundamental disconnect. In this paper, we bridge that gap through effective semantic type discovery and indexing of structured data. We demonstrate S3D (Semantic Search over Structured Data), a novel system that supports queries such as finding related tables, rows or columns, amongst others, on a large scale of structured data from multiple heterogeneous sources.
Feature Extraction for Large-Scale Text Collections
Authors: Luke Gallagher, Antonio Mallia, J. Shane Culpepper, Torsten Suel, Berkant Barla Cambazoglu
Venue: CIKM
Year: 2020

Feature engineering is a fundamental but poorly documented component in Learning-to-Rank (LTR) search engines. Such features are commonly used to construct learning models for web and product search engines, recommender systems, and question-answering tasks. In each of these domains, there is a growing interest in the creation of open-access test collections that promote reproducible research. However, there are still few open-source software packages capable of extracting high-quality machine learning features from large text collections. Instead, most feature-based LTR research relies on "canned" test collections, which often do not expose critical details about the underlying collection or implementation details of the extracted features. Both of these are crucial to collection creation and deployment of a search engine into production. So in this regard, the experiments are rarely reproducible with new features or collections, or helpful for companies wishing to deploy LTR systems. In this paper, we introduce Fxt, an open-source framework to perform efficient and scalable feature extraction. Fxt can easily be integrated into complex, high-performance software applications to help solve a wide variety of text-based machine learning problems. To demonstrate the software's utility, we build and document a reproducible feature extraction pipeline and show how to recreate several common LTR experiments using the ClueWeb09B collection. Researchers and practitioners can benefit from Fxt to extend their machine learning pipelines for various text-based retrieval tasks, and learn how some static document features and query-specific features are implemented.
Rotate3D: Representing Relations as Rotations in Three-Dimensional Space for Knowledge Graph Embedding
Authors: Chang Gao, Chengjie Sun, Lili Shan, Lei Lin, Mingjiang Wang
Venue: CIKM
Year: 2020

Knowledge graph embedding, which aims to learn low-dimensional embeddings of entities and relations, plays a vital role in a wide range of applications. It is crucial for knowledge graph embedding models to model and infer various relation patterns, such as symmetry/antisymmetry, inversion, and composition. However, most existing methods fail to model the non-commutative composition pattern, which is essential, especially for multi-hop reasoning. To address this issue, we propose a new model called Rotate3D, which maps entities to the three-dimensional space and defines relations as rotations from head entities to tail entities. By using the non-commutative composition property of rotations in the three-dimensional space, Rotate3D can naturally preserve the order of the composition of relations. Experiments show that Rotate3D outperforms existing state-of-the-art models for link prediction and path query answering. Further case studies demonstrate that Rotate3D can effectively capture various relation patterns with a marked improvement in modeling the composition pattern.
STREAMER: A Powerful Framework for Continuous Learning in Data Streams
Authors: Sandra Garcia-Rodriguez, Mohammad Alshaer, Cédric Gouy-Pailler
Venue: CIKM
Year: 2020

With the proliferation of continuous data generation, data stream processing has become a key topic in research. As a consequence, the need for dedicated tools to apply continuous learning in streams emerges. This paper presents STREAMER, a flexible, scalable, and cross-platform machine learning experimenter with a realistic operational stream environment and visualization capabilities. Oriented to data scientists, this framework provides a set of machine learning algorithms and an API to easily integrate new ones. In order to illustrate how STREAMER works, we show a demonstration of an unsupervised anomaly detection of electrocardiograms (ECG) tested in a streaming context.
MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data
Authors: Xiaoqing Geng, Xiwen Chen, Kenny Q. Zhu, Libin Shen, Yinggong Zhao
Venue: CIKM
Year: 2020

Few-shot relation classification seeks to classify incoming query instances after meeting only few support instances. This ability is gained by training with large amount of in-domain annotated data. In this paper, we tackle an even harder problem by further limiting the amount of data available at training time. We propose a few-shot learning framework for relation classification, which is particularly powerful when the training data is very small. In this framework, models not only strive to classify query instances, but also seek underlying knowledge about the support instances to obtain better instance representations. The framework also includes a method for aggregating cross-domain knowledge into models by open-source task enrichment. Additionally, we construct a brand new dataset: the TinyRel-CM dataset, a few-shot relation classification dataset in health domain with purposely small training data and challenging relation classes. Experimental results demonstrate that our framework brings performance gains for most underlying classification models, outperforms the state-of-the-art results given small training data, and achieves competitive results with sufficiently large training data.
The Battle Against Online Harmful Information: The Cases of Fake News and Hate Speech
Authors: Anastasia Giachanou, Paolo Rosso
Venue: CIKM
Year: 2020

Social media have given the opportunity to users to express their opinions online in a fast and easy way. The ease of generating content online and the anonymity that social media provide have increased the amount of harmful content that is published. This tutorial will focus on the topic of online harmful information. First, we will analyse and explain the different types of online harmful information with a particular focus on fake news and hate speech. In addition, we will explain the different computational approaches proposed in the literature for the detection of fake news and hate speech. Next, we will present details regarding the evaluation process, datasets and shared tasks and finally, we will discuss future directions in the field of online harmful information detection.
INforE: Interactive Cross-platform Analytics for Everyone
Authors: Nikos Giatrakos, David Arnu, Theodoros Bitsakis, Antonios Deligiannakis, Minos N. Garofalakis, Ralf Klinkenberg, Aris Konidaris, Antonis Kontaxakis, Yannis Kotidis, Vasilis Samoladas, Alkis Simitsis, George Stamatakis, Fabian Temme, Mate Torok, Edwin Yaqub, Arnau Montagud, Miguel Ponce de Leon, Holger Arndt, Stefan Burkard
Venue: CIKM
Year: 2020

We present INforE, a prototype supporting non-expert programmers in performing optimized, cross-platform, streaming analytics at scale. INforE offers: a) a new extension to the RapidMiner Studio for graphical design of Big streaming Data workflows, (b) a novel optimizer to instruct the execution of workflows across Big Data platforms and clusters, (c) a synopses data engine for interactivity at scale via the use of data summaries, (d) a distributed, online data mining and machine learning module. To our knowledge INforE is the first holistic approach in streaming settings. We demonstrate INforE in the fields of life science and financial data analysis.
Estimating Topic Difficulty Using Normalized Discounted Cumulated Gain
Authors: Lukas Gienapp, Benno Stein, Matthias Hagen, Martin Potthast
Venue: CIKM
Year: 2020

Information retrieval evaluation has to consider the varying "difficulty" between topics. Topic difficulty is often defined in terms of the aggregated effectiveness of a set of retrieval systems to satisfy a respective information need. Current approaches to estimate topic difficulty come with drawbacks such as being incomparable across different experimental settings. We introduce a new approach to estimate topic difficulty, which is based on the ratio of systems that achieve an NDCG score that is better than a baseline formed as random ranking of the pool of judged documents. We modify the NDCG measure to explicitly reflect a system's divergence from this hypothetical random ranker. In this way we achieve relative comparability of topic difficulty scores across experimental settings as well as stability to outlier systems?features lacking in previous difficulty estimations. We reevaluate the TREC 2012 Web Track's ad hoc task to demonstrate the feasibility of our approach in practice.
The Impact of Negative Relevance Judgments on NDCG
Authors: Lukas Gienapp, Maik Fröbe, Matthias Hagen, Martin Potthast
Venue: CIKM
Year: 2020

NDCG is one of the most commonly used measures to quantify system performance in retrieval experiments. Though originally not considered, graded relevance judgments nowadays frequently include negative labels. Negative relevance labels cause NDCG to be unbounded. This is probably why widely used implementations of NDCG map negative relevance labels to zero, thus ensuring the resulting scores to originate from the [0,1] range. But zeroing negative labels discards valuable relevance information, e.g., by treating spam documents the same as unjudged ones, which are assigned the relevance label of zero by default. We show that, instead of zeroing negative labels, a min-max-normalization of NDCG retains its statistical power while improving its reliability and stability.
Towards Inferring Queries from Simple and Partial Provenance Examples
Authors: Amir Gilad, Yuval Moskovitch
Venue: CIKM
Year: 2020

The field of query-by-example aims at inferring queries from output examples given by non-expert users, by finding the underlying logic that binds the examples. However, for a very small set of examples, it is difficult to correctly infer such logic. To bridge this gap, previous work suggested attaching explanations to each output example, modeled as provenance, allowing users to explain the reason behind their choice of example. In this paper, we explore the problem of inferring queries from a few output examples and intuitive explanations. We propose a two step framework: (1) convert the explanations into (partial) provenance and (2) infer a query that generates the output examples using a novel algorithm that employs a graph based approach. This framework is suitable for non-experts as it does not require the specification of the provenance in its entirety or an understanding of its structure. We show promising initial experimental results of our approach.
ArXivDigest: A Living Lab for Personalized Scientific Literature Recommendation
Authors: Kristian Gingstad, Øyvind Jekteberg, Krisztian Balog
Venue: CIKM
Year: 2020

Providing personalized recommendations that are also accompanied by explanations as to why an item is recommended is a research area of growing importance. At the same time, progress is limited by the availability of open evaluation resources. In this work, we address the task of scientific literature recommendation. We present arXivDigest, which is an online service providing personalized arXiv recommendations to end users and operates as a living lab for researchers wishing to work on explainable scientific literature recommendations.