OpenRaiser/Intern-Atlas huggingface dataset:
Learning to Profile: User Meta-Profile Network for Few-Shot Learning
Authors: Hao Gong, Qifang Zhao, Tianyu Li, Derek Cho, DuyKhuong Nguyen
Venue: CIKM
Year: 2020

Meta-learning approaches have shown great success in solving challenging knowledge transfer and fast adaptation problems with few samples in vision and language domains. However, few studies discuss the practice of meta-learning for large-scale industrial applications, e.g., representation learning for e-commerce platform users. Although e-commerce companies have spent many efforts on learning accurate and expressive representations to provide a better user experience, we argue that such efforts cannot be stopped at this step. In addition to learning a strong profile of user behaviors, the challenging question about how to effectively transfer the learned representation and quickly adapt the learning process to the subsequent learning tasks or applications is raised simultaneously. This paper introduces the contributions that we made to address these challenges from three aspects. 1) Meta-learning model: In the context of representation learning with e-commerce user behavior data, we propose a meta-learning framework called the Meta-Profile Network, which extends the ideas of matching network and relation network for knowledge transfer and fast adaptation; 2) Encoding strategy: To keep high fidelity of large-scale long-term sequential behavior data, we propose a time-heatmap encoding strategy that allows the model to encode data effectively; 3) Deep network architecture: A multi-modal model combined with multi-task learning architecture is utilized to address the cross-domain knowledge learning and insufficient label problems. Moreover, we argue that an industrial model should not only have good performance in terms of accuracy, but also have better robustness and uncertainty performance under extreme conditions. We evaluate the performance of our model with extensive control experiments in various extreme scenarios, i.e. out-of-distribution detection, data insufficiency and class imbalance scenarios. The Meta-Profile Network shows significant improvement in the model performance when compared to baseline models.
Price Forecast with High-Frequency Finance Data: An Autoregressive Recurrent Neural Network Model with Technical Indicators
Authors: Yuechun Gu, Da Yan, Sibo Yan, Zhe Jiang
Venue: CIKM
Year: 2020

The availability of high-frequency trade data has made it possible for the intraday forecast of price patterns. With the help of technical indicators, recent studies have shown that LSTM based deep learning models are able to predict price directions (a binary classification problem) with performance better than a random guess. However, only naive recurrent networks were adopted, and these works did not compare with the tools used by finance practitioners. Our experiments show that GARCH beats their LSTM models by a large margin. We propose to adopt an autoregressive recurrent network instead so that the loss of the prediction at every time step contributes to the model training; we also treat a rich set of technical indicators at each time step as covariates to enhance the model input. Finally, we treat the problem of price pattern forecast as a regression problem on the price itself; even for price direction prediction, we show that our performance is much better than if we model the problem as binary classification. We show that only when all these designs are adopted, an LSTM model can beat GARCH (and by a large margin). This work corrects the poor use of LSTM networks in recent studies, and provides "the" baseline that is able to fully unleash the power of LSTM for future work to compare with. Moreover, since our model is a price regressor with very good prediction performance, it can serve as a valuable tool for designing trading strategies (including day trading). Our model has been used by quantitative analysts in Freddie Mac for over one quarter, and is found to be more effective than traditional GARCH variants in market prediction.
A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction
Authors: Liyi Guo, Rui Lu, Haoqi Zhang, Junqi Jin, Zhenzhe Zheng, Fan Wu, Jin Li, Haiyang Xu, Han Li, Wenkai Lu, Jian Xu, Kun Gai
Venue: CIKM
Year: 2020

For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue. Therefore, providing better services for advertisers is essential for the long-term prosperity for e-commerce platforms. To achieve this goal, the ad platform needs to have an in-depth understanding of advertisers in terms of both their marketing intents and satisfaction over the advertising performance, based on which further optimization could be carried out to service the advertisers in the correct direction. In this paper, we propose a novel Deep Satisfaction Prediction Network (DSPN), which models advertiser intent and satisfaction simultaneously. It employs a two-stage network structure where advertiser intent vector and satisfaction are jointly learned by considering the features of advertiser's action information and advertising performance indicators. Experiments on an Alibaba advertisement dataset and online evaluations show that our proposed DSPN outperforms state-of-the-art baselines and has stable performance in terms of AUC in the online environment. Further analyses show that DSPN not only predicts advertisers' satisfaction accurately but also learns an explainable advertiser intent, revealing the opportunities to optimize the advertising performance further.
Multi-Model Data Query Languages and Processing Paradigms
Authors: Qingsong Guo, Jiaheng Lu, Chao Zhang, Calvin Sun, Steven Yuan
Venue: CIKM
Year: 2020

Specifying users' interests with a formal query language is a typically challenging task, which becomes even harder in the context of multi-model data management because we have to deal with data variety. It usually lacks a unified schema to help the users issuing their queries, or has an incomplete schema as data come from disparate sources. Multi-Model DataBases (MMDBs) have emerged as a promising approach for dealing with this task as they are capable of accommodating and querying the multi-model data in a single system. This tutorial aims to offer a comprehensive presentation of a wide range of query languages for MMDBs and to make comparisons of their properties from multiple perspectives. We will discuss the essence of cross-model query processing and provide insights on the research challenges and directions for future work. The tutorial will also offer the participants hands-on experience in applying MMDBs to issue multi-model data queries.
Some Issues for Location Dependent Information System Query in Mobile Environment
Authors: Ajay Kr. Gupta
Venue: CIKM
Year: 2020

Location Based Services (LBS) is a continuous, local, and spatially restricted mobile database system (MDS) technology in the sense of a mobile environment. This domain is associated with many fascinating issues and challenges to be tackled and provided a fertile ground for many researchers and experts to work on it. This paper discusses the issues and challenges derived from the available literature to the research community regarding the latest approaches and experimentation results for location-dependent cache invalidation-replacement, prefetching, location privacy, and map matching (MM) policies. It offers potential future paths for exploring the unanswered questions.
Knowledge Graphs: A Tutorial on the History of Knowledge Graph's Main Ideas
Authors: Claudio Gutierrez, Juan F. Sequeda
Venue: CIKM
Year: 2020

Knowledge Graphs can be considered as fulfilling an early vision in Computer Science of creating intelligent systems that integrate knowledge and data at large scale. Stemming from scientific advancements in research areas of Semantic Web, Databases, Knowledge representation, NLP, Machine Learning, among others, Knowledge Graphs have rapidly gained popularity in academia and industry in the past years. The integration of such disparate disciplines and techniques give the richness to Knowledge Graphs, but also present the challenge to practitioners and theoreticians to know how current advances develop from early techniques in order, on one hand, take full advantage of them, and on the other, avoid reinventing the wheel. This tutorial will provide a historical context on the roots of Knowledge Graphs grounded in the advancements of Logic, Data and the combination thereof.
IAI MovieBot: A Conversational Movie Recommender System
Authors: Javeria Habib, Shuo Zhang, Krisztian Balog
Venue: CIKM
Year: 2020

Conversational recommender systems support users in accomplishing recommendation-related goals via multi-turn conversations. To better model dynamically changing user preferences and provide the community with a reusable development framework, we introduce IAI MovieBot, a conversational recommender system for movies. It features a task-specific dialogue flow, a multi-modal chat interface, and an effective way to deal with dynamically changing user preferences. The system is made available open source and is operated as a channel on Telegram.
Exploration of Dynamic Query-Based Load Balancing for Partially Replicated Database Systems with Node Failures
Authors: Stefan Halfpap, Rainer Schlosser
Venue: CIKM
Year: 2020

Database replication is a mechanism to achieve scalability, for example, by executing queries independently on replica nodes. Partial replication is an approach to minimize the overall memory consumption of a replication cluster while still enabling a balanced load distribution among nodes to scale the query throughput linearly with the number of replicas. Partial replication reduces the cluster costs, speeds up data synchronization, and improves caching. However, load balancing may become skewed in the case of unexpected query distributions, unfavorable query timings, or node failures. To simulate and visualize the load balancing behavior for specific data fragment allocations, we implemented an interactive application. It allows users to retrace and evaluate the end-to-end performance of partially replicated database systems in varying experiments. Using our tool, we find that existing allocation approaches are either not memory-efficient or may result in load imbalances when nodes fail. We show that our novel robust allocation strategy achieves a better workload distribution with even less memory.
GAEAT: Graph Auto-Encoder Attention Networks for Knowledge Graph Completion
Authors: Yanfei Han, Quan Fang, Jun Hu, Shengsheng Qian, Changsheng Xu
Venue: CIKM
Year: 2020

Knowledge graph embedding (KGE) encodes components of a KG including entities and relations into continuous low vector space. Most existing methods focus on treating entities and relations in triples independently and thus failing to capture the complex and hidden information that is inherently implicit inside the local neighborhood surrounding a triple. In this paper, we present a new approach for knowledge graph completion called GAEAT (Graph Auto-encoder Attention Network Embedding), which can encapsulate both entity and relation features. Specifically, we construct a triple-level auto-encoder by extending graph attention mechanisms to obtain latent representations of entities and relations simultaneously. To justify our proposed model, we evaluate GAEAT on two real-world datasets. The experimental results demonstrate that GAEAT can outperform state-of-the-art KGE models in knowledge graph completion task, which validates the effectiveness of GAEAT. The source code of this paper can be obtained from https://github.com/TomersHan/GAEAT.
Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network
Authors: Zhenyu Han, Fengli Xu, Jinghan Shi, Yu Shang, Haorui Ma, Pan Hui, Yong Li
Venue: CIKM
Year: 2020

In the past decade, the heterogeneous information network (HIN) has become an important methodology for modern recommender systems. To fully leverage its power, manually designed network templates, i.e., meta-structures, are introduced to filter out semantic-aware information. The hand-crafted meta-structure rely on intense expert knowledge, which is both laborious and data-dependent. On the other hand, the number of meta-structures grows exponentially with its size and the number of node types, which prohibits brute-force search. To address these challenges, we propose Genetic Meta-Structure Search (GEMS) to automatically optimize meta-structure designs for recommendation on HINs. Specifically, GEMS adopts a parallel genetic algorithm to search meaningful meta-structures for recommendation, and designs dedicated rules and a meta-structure predictor to efficiently explore the search space. Finally, we propose an attention based multi-view graph convolutional network module to dynamically fuse information from different meta-structures. Extensive experiments on three real-world datasets suggest the effectiveness of GEMS, which consistently outperforms all baseline methods in HIN recommendation. Compared with simplified GEMS which utilizes hand-crafted meta-paths, GEMS achieves over 6% performance gain on most evaluation metrics. More importantly, we conduct an in-depth analysis on the identified meta-structures, which sheds light on the HIN based recommender system design.
Privacy-Preserving Classification with Secret Vector Machines
Authors: Valentin Hartmann, Konark Modi, Josep M. Pujol, Robert West
Venue: CIKM
Year: 2020

Today, large amounts of valuable data are distributed among millions of user-held devices, such as personal computers, phones, or Internet-of-things devices. Many companies collect such data with the goal of using it for training machine learning models allowing them to improve their services. User-held data is, however, often sensitive, and collecting it is problematic in terms of privacy. We address this issue by proposing a novel way of training a supervised classifier in a distributed setting akin to the recently proposed federated learning paradigm, but under the stricter privacy requirement that the server that trains the model is assumed to be untrusted and potentially malicious. We thus preserve user privacy by design, rather than by trust. In particular, our framework, called secret vector machine (SecVM), provides an algorithm for training linear support vector machines (SVM) in a setting in which data-holding clients communicate with an untrusted server by exchanging messages designed to not reveal any personally identifiable information. We evaluate our model in two ways. First, in an offline evaluation, we train SecVM to predict user gender from tweets, showing that we can preserve user privacy without sacrificing classification performance. Second, we implement SecVM's distributed framework for the Cliqz web browser and deploy it for predicting user gender in a large-scale online evaluation with thousands of clients, outperforming baselines by a large margin and thus showcasing that SecVM is suitable for production environments.
TiCCo: Time-Centric Content Exploration
Authors: Philip Hausner, Dennis Aumiller, Michael Gertz
Venue: CIKM
Year: 2020

Time is a natural way to order information and can be utilized to summarize events and to construct a chronology of contents within a document collection in many application domains. Structuring the sequence of events along a timeline allows users to grasp information at-a-glance, which enables them to get familiar with a topic in only a short amount of time and can hence support the analysis of more complicated and heterogeneous textual data. The manual construction of timelines, however, is a tedious and error-prone task, leading to static timeline representations that limit users to a passive role. In this paper, TiCCo, an automated extraction pipeline from arbitrary English and German text collections, is provided and presented to the user in an interactive manner. This puts the user in an active role in which she not only absorbs knowledge, but also influences in which ways the information is presented to her. In-depth investigations of a specific point in time are augmented by utilizing time-centric co-occurrence graphs that further summarize information extracted from a document collection, and enable users to explore the chronology of events by allowing them to interact with the constructed graphs as well as the underlying documents.
VN Network: Embedding Newly Emerging Entities with Virtual Neighbors
Authors: Yongquan He, Zihan Wang, Peng Zhang, Zhaopeng Tu, Zhaochun Ren
Venue: CIKM
Year: 2020

Embedding entities and relations into continuous vector spaces has attracted a surge of interest in recent years. Most embedding methods assume that all test entities are available during training, which makes it time-consuming to retrain embeddings for newly emerging entities. To address this issue, recent works apply the graph neural network on the existing neighbors of the unseen entities. In this paper, we propose a novel framework, namely Virtual Neighbor (VN) network, to address three key challenges. Firstly, to reduce the neighbor sparsity problem, we introduce the concept of the virtual neighbors inferred by rules. And we assign soft labels to these neighbors by solving a rule-constrained problem, rather than simply regarding them as unquestionably true. Secondly, many existing methods only use one-hop or two-hop neighbors for aggregation and ignore the distant information that may be helpful. Instead, we identify both logic and symmetric path rules to capture complex patterns. Finally, instead of one-time injection of rules, we employ an iterative learning scheme between the embedding method and virtual neighbor prediction to capture the interactions within. Experimental results on two knowledge graph completion tasks demonstrate that our VN network significantly outperforms state-of-the-art baselines. Furthermore, results on Subject/Object-R show that our proposed VN network is highly robust to the neighbor sparsity problem.
Learning to Re-Rank with Contextualized Stopwords
Authors: Sebastian Hofstätter, Aldo Lipani, Markus Zlabinger, Allan Hanbury
Venue: CIKM
Year: 2020

The use of stopwords has been thoroughly studied in traditional Information Retrieval systems, but remains unexplored in the context of neural models. Neural re-ranking models take the full text of both the query and document into account. Naturally, removing tokens that do not carry relevance information provides us with an opportunity to improve the effectiveness by reducing noise and lower document representation caching-storage requirements. In this work we propose a novel contextualized stopword detection mechanism for neural re-ranking models. This mechanism consists of training a sparse vector in order to filter out document tokens from the ranking decision. This vector is learned end-to-end based on the contextualized document representations, allowing the model to filter terms on a per occurrence basis. This leads to a more explainable model, as it reduces noise. We integrate our component into the state-of-the-art interaction-based TK neural re-ranking model. Our experiments on the MS MARCO passage collection and queries from the TREC 2019 Deep Learning Track show that filtering out traditional stopwords prior to the neural model reduces its effectiveness, while learning to filter out contextualized representations improves it.
Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering
Authors: Sebastian Hofstätter, Markus Zlabinger, Mete Sertkan, Michael Schröder, Allan Hanbury
Venue: CIKM
Year: 2020

There are many existing retrieval and question answering datasets. However, most of them either focus on ranked list evaluation or single-candidate question answering. This divide makes it challenging to properly evaluate approaches concerned with ranking documents and providing snippets or answers for a given query. In this work, we present FiRA: a novel dataset of Fine-Grained Relevance Annotations. We extend the ranked retrieval annotations of the Deep Learning track of TREC 2019 with passage and word level graded relevance annotations for all relevant documents. We use our newly created data to study the distribution of relevance in long documents, as well as the attention of annotators to specific positions of the text. As an example, we evaluate the recently introduced TKL document ranking model. We find that although TKL exhibits state-of-the-art retrieval results for long documents, it misses many relevant passages.
IWILDS'20: The 1st International Workshop on Investigating Learning during Web Search
Authors: Anett Hoppe, Ran Yu, Yvonne Kammerer, Ladislao Salmerón
Venue: CIKM
Year: 2020

Web search is one of the most ubiquitous online activities and often used for learning purposes, i.e., to extend one's knowledge or skills about certain topics or procedures. The importance of learning as an outcome of Web search has been recognized in research at the intersection of information retrieval, human-computer interaction, psychology, and educational sciences. Search as Learning (SAL) research examines relationships between querying, navigation, and reading behavior during Web search and the resulting learning outcomes, and how they can be measured, predicted, and supported. IWILDS aims to provide a platform to the interdisciplinary SAL community, with the objective to bring together interested researchers, provide room for presentation and discussion of novel research insights, and to inspire future directions of SAL research.
WMEgo: Willingness Maximization for Ego Network Data Extraction in Online Social Networks
Authors: Bay-Yuan Hsu, Chih-Ya Shen, Ming-Yi Chang
Venue: CIKM
Year: 2020

The data of egocentric networks (ego networks) are very important for evaluating and validating the algorithms and machine learning approaches in Online Social Networks (OSNs). Nevertheless, obtaining the ego network data from OSNs is not a trivial task. Conventional manual approaches are time-consuming, and only a small number of users would agree to contribute their data. This is because there are two important factors that should be considered simultaneously for this data acquisition task: i) users' willingness to contribute their data, and ii) the structure of the ego network. However, addressing the above two factors to obtain the more complete ego network data has not received much research attention. Therefore, in this paper, we make our first attempt to address this issue by proposing a new research problem, named Willingness Maximization for Ego Network Extraction in Online Social Networks (WMEgo), to identify a set of ego networks from the OSN such that the willingness of the users to contribute their data is maximized. We prove that WMEgo is NP-hard and propose a 1/2*(1 1/e)-approximation algorithm, named Ego Network Identification with Maximum Willingness (EIMW). We conduct an evaluation study with 672 volunteers to validate the proposed WMEgo and EIMW, and perform extensive experiments on multiple real datasets to demonstrate the effectiveness and efficiency of our approach.