OpenRaiser/Intern-Atlas huggingface dataset:
DTCDR: A Framework for Dual-Target Cross-Domain Recommendation
Authors: Feng Zhu, Chaochao Chen, Yan Wang, Guanfeng Liu, Xiaolin Zheng
Venue: CIKM
Year: 2019

In order to address the data sparsity problem in recommender systems, in recent years, Cross-Domain Recommendation (CDR) leverages the relatively richer information from a source domain to improve the recommendation performance on a target domain with sparser information. However, each of the two domains may be relatively richer in certain types of information (e.g., ratings, reviews, user profiles, item details, and tags), and thus, if we can leverage such information well, it is possible to improve the recommendation performance on both domains simultaneously (i.e., dual-target CDR), rather than a single target domain only. To this end, in this paper, we propose a new framework, DTCDR, for Dual-Target Cross-Domain Recommendation. In DTCDR, we first extensively utilize rating and multi-source content information to generate rating and document embeddings of users and items. Then, based on Multi-Task Learning (MTL), we design an adaptable embedding-sharing strategy to combine and share the embeddings of common users across domains, with which DTCDR can improve the recommendation performance on both richer and sparser (i.e., dual-target) domains simultaneously. Extensive experiments conducted on real-world datasets demonstrate that DTCDR can significantly improve the recommendation accuracies on both richer and sparser domains and outperform the state-of-the-art single-domain and cross-domain approaches.
Learning to Ask: Question-based Sequential Bayesian Product Search
Authors: Jie Zou, Evangelos Kanoulas
Venue: CIKM
Year: 2019

Product search is generally recognized as the first and foremost stage of online shopping and thus significant for users and retailers of e-commerce. Most of the traditional retrieval methods use some similarity functions to match the user's query and the document that describes a product, either directly or in a latent vector space. However, user queries are often too general to capture the minute details of the specific product that a user is looking for. In this paper, we propose a novel interactive method to effectively locate the best matching product. The method is based on the assumption that there is a set of candidate questions for each product to be asked. In this work, we instantiate this candidate set by making the hypothesis that products can be discriminated by the entities that appear in the documents associated with them. We propose a Question-based Sequential Bayesian Product Search method, QSBPS, which directly queries users on the expected presence of entities in the relevant product documents. The method learns the product relevance as well as the reward of the potential questions to be asked to the user by being trained on the search history and purchase behavior of a specific user together with that of other users. The experimental results show that the proposed method can greatly improve the performance of product search compared to the state-of-the-art baselines.
MarlRank: Multi-agent Reinforced Learning to Rank
Authors: Shihao Zou, Zhonghua Li, Mohammad Akbari, Jun Wang, Peng Zhang
Venue: CIKM
Year: 2019

When estimating the relevancy between a query and a document, ranking models largely neglect the mutual information among documents. A common wisdom is that if two documents are similar in terms of the same query, they are more likely to have similar relevance score. To mitigate this problem, in this paper, we propose a multi-agent reinforced ranking model, named MarlRank. In particular, by considering each document as an agent, we formulate the ranking process as a multi-agent Markov Decision Process (MDP), where the mutual interactions among documents are incorporated in the ranking process. To compute the ranking list, each document predicts its relevance to a query considering not only its own query-document features but also its similar documents' features and actions. By defining reward as a function of NDCG, we can optimize our model directly on the ranking performance measure. Our experimental results on two LETOR benchmark datasets show that our model has significant performance gains over the state-of-art baselines. We also find that the NDCG shows an overall increasing trend along with the step of interactions, which demonstrates that the mutual information among documents helps improve the ranking performance.
Minimal Edit-Based Diffs for Large Trees
Authors: Mateusz Pawlik, Nikolaus Augsten
Venue: CIKM
Year: 2020

Hierarchically structured data are commonly represented as trees and have given rise to popular data formats like XML or JSON. An interesting query computes the difference between two versions of a tree, expressed as the minimum set of node edits (deletion, insertion, label rename) that transform one tree into another, commonly known as the tree edit distance. Unfortunately, the fastest tree edit distance algorithms run in cubic time and quadratic space and are therefore not feasible for large inputs. In this paper, we leverage the fact that the difference between two versions of a tree is typically much smaller than the overall tree size. We propose a new tree edit distance algorithm that is linear in the tree size for similar trees. Our algorithm is based on the new concept of top node pairs and avoids redundant distance computations, the main issue with previous solutions for tree diffs. We empirically evaluate the runtime of our algorithm on large synthetic and real-world trees; our algorithm clearly outperforms the state of the art, often by orders of magnitude.
Weaving Text into Tables
Authors: Dhruv Gupta, Klaus Berberich
Venue: CIKM
Year: 2020

In this paper, we showcase JIGSAW, a system that is able to shape unstructured text into structured tables for user-defined schemas. In short, to structure text into tables, JIGSAW leverages the lexico-syntactic structure imposed by linguistic annotations (e.g., part-of-speech, named entities, temporal and numerical expressions) on natural language text. We describe how challenging knowledge-centric tasks such as question answering, summarization, and analytics can be greatly simplified with the help of JIGSAW.
Partial Relationship Aware Influence Diffusion via a Multi-channel Encoding Scheme for Social Recommendation
Authors: Bo Jin, Ke Cheng, Liang Zhang, Yanjie Fu, Minghao Yin, Lu Jiang
Venue: CIKM
Year: 2020

Social recommendation tasks exploit social connections to enhance recommendation performance. To fully utilize each user's first-order and high-order neighborhood preferences, recent approaches incorporate influence diffusion process for better user preference modeling. Despite the superior performance of these models, they either neglect the latent individual interests hidden in the user-item interactions or rely on computationally expensive graph attention models to uncover the item-induced sub-relations, which essentially determine the influence propagation passages. Considering the sparse substructures are derived from original social network, we name them as partial relationships between users. We argue such relationships can be directly modeled such that both personal interests and shared interests can propagate along a few channels (or dimensions) of latent users' embeddings. To this end, we propose a partial relationship aware influence diffusion structure via a computationally efficient multi-channel encoding scheme. Specifically, the encoding scheme first simplifies graph attention operation based on a channel-wise sparsity assumption, and then adds an InfluenceNorm function to maintain such sparsity. Moreover, ChannelNorm is designed to alleviate the oversmoothing problem in graph neural network models. Extensive experiments on two benchmark datasets show that our method is comparable to state-of-the-art graph attention-based social recommendation models while capturing user interests according to partial relationships more efficiently.
3rd International Workshop on EntitY Retrieval and lEarning (EYRE 2020)
Authors: Gong Cheng, Kalpa Gunaratna, Jun Wang
Venue: CIKM
Year: 2020

Entity retrieval has received increasing research attention. The recent progress in deep and machine learning techniques provides powerful tools for developing effective entity-centered solutions. This workshop series provides a platform where interdisciplinary studies of entity retrieval and learning can be presented, and focused discussions can take place. We also organize a shared task related to entity retrieval. The 3rd International Workshop on EntitY Retrieval and lEarning (EYRE 2020) was a half-day workshop co-located with the 29th ACM International Conference on Information and Knowledge Management (CIKM 2020) as a virtual event in Ireland.
Dual Head-wise Coattention Network for Machine Comprehension with Multiple-Choice Questions
Authors: Zhuang Liu, Kaiyu Huang, Degen Huang, Zhuang Liu, Jun Zhao
Venue: CIKM
Year: 2020

Multiple-choice Machine Comprehension (MC) is an important and challenging nature language processing (NLP) task where the machine is required to make the best answer from candidate answer set given particular passage and question. Existing approaches either only utilize the powerful pre-trained language models or only rely on an over complicated matching network that is design supposed to capture the relationship effectively among the triplet of passage, question and candidate answers. In this paper, we present a novel architecture, Dual Head-wise Coattention network (called DHC), which is a simple and efficient attention neural network designed to perform multiple-choice MC task. Our proposed DHC not only support a powerful pre-trained language model as encoder, but also models the MC relationship as attention mechanism straightforwardly, by head-wise matching and aggregating method on multiple layers, which better model relationships sufficiently between question and passage, and cooperate with large pre-trained language models more efficiently. To evaluate the performance, we test our proposed model on five challenging and well-known datasets for multiple-choice MC: RACE, DREAM, SemEval-2018 Task 11, OpenBookQA, and TOEFL. Extensive experimental results demonstrate that our proposal can achieve a significant increase in accuracy comparing existing models based on all five datasets, and it consistently outperforms all tested baselines including the state-of-the-arts techniques. More remarkably, our proposal is a pluggable and more flexible model, and it thus can be plugged into any pre-trained Language Models based on BERT. Ablation studies demonstrate its state-of-the-art performance and generalization.
Fairness in Unsupervised Learning
Authors: Deepak P, Joemon M. Jose, Sanil V
Venue: CIKM
Year: 2020

Data in digital form is expanding at an exponential rate, far outpacing any chance of getting any significant fraction labelled manually. This has resulted in heightened research emphasis on unsupervised learning, learning in the absence of labels. In fact, unsupervised learning has been often dubbed as the next frontier of AI. Unsupervised learning is the most plausible model to analyze the bulk of passively collected data that spans across various domains; e.g., social media footprints, safety/surveilance cameras, IoT devices, sensors, smartphone apps, medical wearables, traffic sensing devices and public wi-fi access. While fairness in supervised learning, such as classification tasks, has inspired a large amount of research in the past few years, work on fair unsupervised learning has been relatively slow in picking up. This tutorial targets to provide an overview of: (i) fairness issues in unsupervised learning drawing abundantly from political philosophy, (ii) current research in fair unsupervised learning, and (iii) new directions to extend the state-of-the-art in fair unsupervised learning. While we intend to broadly cover all tasks in unsupervised learning, our focus will be on clustering, retrieval and representation learning. In a unique departure from conventional data science tutorials, we will place significant emphasis on presenting and debating pertinent literature from ethics and philosophy. Overall, this half-day tutorial brings a strong emphasis on ensuring strong interdisciplinarity.
Two-Stage Audience Expansion for Financial Targeting in Marketing
Authors: Zhining Liu, Xiao-Fan Niu, Chenyi Zhuang, Yize Tan, Yixiang Mu, Jinjie Gu, Guannan Zhang
Venue: CIKM
Year: 2020

With the revolution of mobile internet, online finance has grown explosively. In this new area, one challenge of significant importance is how to effectively deliver the financial products or services to a set of target users by marketing. Given a product or service to be promoted and a set of users as seeds, audience expansion is such a targeting technique, which aims to find potential audience among a large number of users. However, in the context of finance, financial products and services are dynamic in nature as they co-vary with the socio-economic environment. Moreover, marketing campaigns for promoting products or services always consist of different rules of play, even for the same type of products or services. As a result, there is a strong demand for the timeliness of seeds in financial targeting. Conventional one-stage audience expansion methods, which generate expanded users by expanding over seeds, would encounter two problems under this setting: (1) the seeds would inevitably involve a number of users that are not representative for expansion, and direct expansion over these noisy seeds would dramatically deteriorate the performance; (2) one-stage expansion over fixed seeds cannot timely and accurately capture users' preferences over the currently running campaign due to the lack of timeliness of seeds. To address the above challenges, in this paper, we present a novel two-stage audience expansion system Hubble. In the first cold-start stage, a reweighting mechanism is devised to suppress the noises within seeds, which is motivated from the observation on the relationship between golden seeds and their corresponding density in the embedding space. With incrementally collecting feedbacks from users, we further include these feedbacks to guide subsequent audience expansion in the second stage. But the distribution of these feedbacks is usually biased and cannot fully characterize the distribution of all target audiences. Therefore, we propose a method to incorporate biased feedbacks with seeds in a meta-learning manner to pan for golden seeds from the noisy seed-set. Finally, we conduct extensive experiments on three real datasets and online A/B testing, which demonstrate the effectiveness of the proposed method. In addition, we release two datasets for boosting the study of this new research topic.
Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network
Authors: Haotian Li, Huan Wei, Yong Wang, Yangqiu Song, Huamin Qu
Venue: CIKM
Year: 2020

Student performance prediction is critical to online education. It can benefit many downstream tasks on online learning platforms, such as estimating dropout rates, facilitating strategic intervention, and enabling adaptive online learning. Interactive online question pools provide students with interesting interactive questions to practice their knowledge in online education. However, little research has been done on student performance prediction in interactive online question pools. Existing work on student performance prediction targets at online learning platforms with predefined course curriculum and accurate knowledge labels like MOOC platforms, but they are not able to fully model knowledge evolution of students in interactive online question pools. In this paper, we propose a novel approach using Graph Neural Networks (GNNs) to achieve better student performance prediction in interactive online question pools. Specifically, we model the relationship between students and questions using student interactions to construct the student-interaction-question network and further present a new GNN model, called R2GCN, which intrinsically works for the heterogeneous networks, to achieve generalizable student performance prediction in interactive online question pools. We evaluate the effectiveness of our approach on a real-world dataset consisting of 104,113 mouse trajectories generated in the problem-solving process of over 4,000 students on 1,631 questions. The experiment results show that our approach can achieve a much higher accuracy of student performance prediction than both traditional machine learning approaches and GNN models.
Learning from Textual Data in Database Systems
Authors: Michael Günther, Philipp Oehme, Maik Thiele, Wolfgang Lehner
Venue: CIKM
Year: 2020

Relational database systems hold massive amounts of text, valuable for many machine learning (ML) tasks. Since ML techniques depend on numerical input representations, pre-trained word embeddings are increasingly utilized to convert text values into meaningful numbers. However, a naïve one-to-one mapping of each word in a database to a word embedding vector misses incorporating rich context information given by the database schema. Thus, we propose a novel relational retrofitting framework Retro to learn numerical representations of text values in databases, capturing the rich information encoded by pre-trained word embedding models as well as context information provided by tabular and foreign key relations in the database. We defined relation retrofitting as an optimization problem, present an efficient algorithm solving it, and investigate the influence of various hyperparameters. Further, we develop simple feed-forward and complex graph convolutional neural network architectures to operate on those representations. Our evaluation shows that the proposed embeddings and models are ready-to-use for many ML tasks, such as text classification, imputation, and link prediction, and even outperform state-of-the-art techniques.
Providing Direct Answers in Search Results: A Study of User Behavior
Authors: Zhijing Wu, Mark Sanderson, Berkant Barla Cambazoglu, W. Bruce Croft, Falk Scholer
Venue: CIKM
Year: 2020

To study the impact of providing direct answers in search results on user behavior, we conducted a controlled user study to analyze factors including reading time, eye-tracked attention, and the influence of the quality of answer module content. We also studied a more advanced answer interface, where multiple answers are shown on the search engine results page (SERP). Our results show that users focus more extensively than normal on the top items in the result list when answers are provided. The existence of the answer module helps to improve user engagement on SERPs, reduces user effort, and promotes user satisfaction during the search process. Furthermore, we investigate how the question type -- factoid or non-factoid -- affects user interaction patterns. This work provides insight into the design of SERPs that includes direct answers to queries, including when answers should be shown.
Modelling User Behavior Dynamics with Embeddings
Authors: Lei Han, Alessandro Checco, Djellel Eddine Difallah, Gianluca Demartini, Shazia Sadiq
Venue: CIKM
Year: 2020

Understanding user interaction behaviors remains a challenging problem. Quantifying behavior dynamics over time as users complete tasks has only been done in specific domains. In this paper, we present a user behavior model built using behavior embeddings to compare behaviors and their change over time. To this end, we first define the formal model and train the model using both action (e.g., copy/paste) embeddings and user interaction feature (e.g., length of the copied text) embeddings. Having obtained vector representations of user behaviors, we then define three measurements to model behavior dynamics over time, namely: behavior position, displacement, and velocity. To evaluate the proposed methodology, we use three real world datasets: (i) tens of users completing complex data curation tasks in a lab setting, (ii) hundreds of crowd workers completing structured tasks in a crowdsourcing setting, and (iii) thousands of editors completing unstructured editing tasks on Wikidata. Through these datasets, we show that the proposed methodology can: (i) surface behavioral differences among users; (ii) recognize relative behavioral changes; and (iii) discover directional deviations of user behaviors. Our approach can be used (i) to capture behavioral semantics from data in a consistent way, (ii) to quantify behavioral diversity for a task and among different users, and (iii) to explore the temporal behavior evolution with respect to various task properties (e.g., structure and difficulty).
Efficient Sampling Algorithms for Approximate Temporal Motif Counting
Authors: Jingjing Wang, Yanhao Wang, Wenjun Jiang, Yuchen Li, Kian-Lee Tan
Venue: CIKM
Year: 2020

A great variety of complex systems ranging from user interactions in communication networks to transactions in financial markets can be modeled as temporal graphs, which consist of a set of vertices and a series of timestamped and directed edges. Temporal motifs in temporal graphs are generalized from subgraph patterns in static graphs which take into account edge orderings and durations in addition to structures. Counting the number of occurrences of temporal motifs is a fundamental problem for temporal network analysis. However, existing methods either cannot support temporal motifs or suffer from performance issues. In this paper, we focus on approximate temporal motif counting via random sampling. We first propose a generic edge sampling (ES) algorithm for estimating the number of instances of any temporal motif. Furthermore, we devise an improved EWS algorithm that hybridizes edge sampling with wedge sampling for counting temporal motifs with 3 vertices and 3 edges. We provide comprehensive analyses of the theoretical bounds and complexities of our proposed algorithms. Finally, we conduct extensive experiments on several real-world datasets, and the results show that our ES and EWS algorithms have higher efficiency, better accuracy, and greater scalability than the state-of-the-art sampling method for temporal motif counting.
Hyper-Substructure Enhanced Link Predictor
Authors: Jian Zhang, Jun Zheng, Jinyin Chen, Qi Xuan
Venue: CIKM
Year: 2020

Link prediction has long been the focus in the analysis of network-structured data. Though straightforward and efficient, heuristic approaches like Common Neighbors perform link prediction with pre-defined assumptions and only use superficial structural features. While it is widely acknowledged that a vertex could be characterized by a bunch of neighbor vertices, network embedding algorithms and newly emerged graph neural networks still exploit structural features on the whole network, which may inevitably bring in noises and limits the scalability of those methods. In this paper, we propose an end-to-end deep learning framework, namely hyper-substructure enhanced link predictor (HELP), for link prediction. HELP utilizes local topological structures from the neighborhood of the given vertex pairs, avoiding useless features. For further exploiting higher-order structural information, HELP also learns features from hyper-substructure network (HSN).Extensive experiments on six benchmark datasets have shown the state-of-the-art performance of HELP on link prediction.