OpenRaiser/Intern-Atlas huggingface dataset:
Collective Link Prediction Oriented Network Embedding with Hierarchical Graph Attention
Authors: Yizhu Jiao, Yun Xiong, Jiawei Zhang, Yangyong Zhu
Venue: CIKM
Year: 2019

To enjoy more social network services, users nowadays are usually involved in multiple online sites at the same time. Aligned social networks provide more information to alleviate the problem of data insufficiency. In this paper, we target on the collective link prediction problem and aim to predict both the intra-network social links as well as the inter-network anchor links across multiple aligned social networks. It is not an easy task, and the major challenges involve the network characteristic difference problem and different directivity properties of the social and anchor links to be predicted. To address the problem, we propose an application oriented network embedding framework, Hierarchical Graph Attention based Network Embedding (HGANE), for collective link prediction over directed aligned networks. Very different from the conventional general network embedding models, HGANE effectively incorporates the collective link prediction task objectives into consideration. It learns the representations of nodes by aggregating information from both the intra-network neighbors (connected by social links) and inter-network partners (connected by anchor links). What's more, we introduce a hierarchical graph attention mechanism for the intra-network neighbors and inter-network partners respectively, which resolves the network characteristic differences and the link directivity challenges effectively. Extensive experiments have been conducted on real-world aligned networks datasets to demonstrate that our model outperformed the state-of-the-art baseline methods in addressing the collective link prediction problem by a large margin.
MusicBot: Evaluating Critiquing-Based Music Recommenders with Conversational Interaction
Authors: Yucheng Jin, Wanling Cai, Li Chen, Nyi Nyi Htun, Katrien Verbert
Venue: CIKM
Year: 2019

Critiquing-based recommender systems aim to elicit more accurate user preferences from users' feedback toward recommendations. However, systems using a graphical user interface (GUI) limit the way that users can critique the recommendation. With the rise of chatbots in many application domains, they have been regarded as an ideal platform to build critiquing-based recommender systems. Therefore, we present MusicBot, a chatbot for music recommendations, featured with two typical critiquing techniques, user-initiated critiquing (UC) and system-suggested critiquing (SC). By conducting a within-subjects (N=45) study with two typical scenarios of music listening, we compared a system of only having UC with a hybrid critiquing system that combines SC with UC. Furthermore, we analyzed the effects of four personal characteristics,musical sophistication (MS), desire for control (DFC), chatbot experience (CE), and tech savviness (TS), on the user's perception and interaction of the recommendation in MusicBot. In general, compared with UC, SC yields higher perceived diversity and efficiency in looking for songs; combining UC and SC tends to increase user engagement. Both MS and DFC positively influence several key user experience (UX) metrics of MusicBot such as interest matching, perceived controllability, and intent to provide feedback.
Learning Region Similarity over Spatial Knowledge Graphs with Hierarchical Types and Semantic Relations
Authors: Xiongnan Jin, Byungkook Oh, Sanghak Lee, Dongho Lee, Kyong-Ho Lee, Liang Chen
Venue: CIKM
Year: 2019

A large number of spatial knowledge graphs (SKGs) are available from spatially enriched knowledge bases, e.g., DBpedia and YAGO2. This provides a great chance to understand valuable information about the regions surrounding us. However, it is hard to comprehend SKGs due to the explosively growing volume and the complication of the graph structures. Thus we study the problem of similar region search (SRS), which is an easy-to-use but effective way to explore spatial data. The effectiveness of SRS highly depends on how to measure the region similarity. However, existing approaches cannot make use of the rich information contained in SKGs thus may lead to incorrect results. In this paper, we propose a spatial knowledge representation learning method for region similarity, namely SKRL4RS. SKRL4RS firstly encodes the spatial entities of an SKG into a vector space to make it easier to extract useful features. Then regions are represented by 3-D tensors using the spatial entity embeddings together with geographical information. Finally, region tensors are fed into the conventional triplet network to learn the feature vectors of regions. The region similarity measure learned by SKRL4RS can capture the hierarchical types, semantic relatedness, and relative locations of spatial entities inside a region. Experimental results on two real-world datasets show that our SKRL4RS outperforms the state-of-the-art by a significant margin in terms of the accuracy of measuring region similarity.
Social Cards Probably Provide For Better Understanding Of Web Archive Collections
Authors: Shawn M. Jones, Michele C. Weigle, Michael L. Nelson
Venue: CIKM
Year: 2019

Used by a variety of researchers, web archive collections have become invaluable sources of evidence. If a researcher is presented with a web archive collection that they did not create, how do they know what is inside so that they can use it for their own research? Search engine results and social media links are represented as surrogates, small easily digestible summaries of the underlying page. Search engines and social media have a different focus, and hence produce different surrogates than web archives. Search engine surrogates help a user answer the question "Will this link meet my information need?" Social media surrogates help a user decide "Should I click on this?" Our use case is subtly different. We hypothesize that groups of surrogates together are useful for summarizing a collection. We want to help users answer the question of "What does the underlying collection contain?" But which surrogate should we use? With Mechanical Turk participants, we evaluate six different surrogate types against each other. We find that the type of surrogate does not influence the time to complete the task we presented the participants. Of particular interest are social cards, surrogates typically found on social media, and browser thumbnails, screen captures of web pages rendered in a browser. At p=0.0569, and p=0.0770, respectively, we find that social cards and social cards paired side-by-side with browser thumbnails probably provide better collection understanding than the surrogates currently used by the popular Archive-It web archiving platform. We measure user interactions with each surrogate and find that users interact with social cards less than other types. The results of this study have implications for our web archive summarization work, live web curation platforms, social media, and more.
PRIVATA: Differentially Private Data Market Framework using Negotiation-based Pricing Mechanism
Authors: Kangsoo Jung, Junkyu Lee, Kunyoung Park, Seog Park
Venue: CIKM
Year: 2019

As the value of digital data increases, the data market is in the spotlight as a means of obtaining a personal information. However, the collection of personal information makes a serious privacy violation and it is a serious problem in the use of personal data. Differential privacy, which is a de-facto standard for privacy protection in statistical databases, can be applied to solve the privacy violation problem. To apply differential privacy to the data market, the amount of noise and corresponding data price should be determined between the provider and consumer. However, this matter has not yet been studied. In this work, we introduce a Privata which is a differentially private data market framework to set the appropriate price and noise parameter in the data market environment. The Privata is based on negotiation technique using Rubinstein bargaining considering social welfare to prevent unfair transactions. We explain the Privata overview and negotiation technique in Privata, and show the Privata implementation.
Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users
Authors: SeongKu Kang, Junyoung Hwang, Dongha Lee, Hwanjo Yu
Venue: CIKM
Year: 2019

Providing accurate recommendations to newly joined users (or potential users, so-called cold-start users) has remained a challenging yet important problem in recommender systems. To infer the preferences of such cold-start users based on their preferences observed in other domains, several cross-domain recommendation (CDR) methods have been studied. The state-of-the-art Embedding and Mapping approach for CDR (EMCDR) aims to infer the latent vectors of cold-start users by supervised mapping from the latent space of another domain. In this paper, we propose a novel CDR framework based on semi-supervised mapping, called SSCDR, which effectively learns the cross-domain relationship even in the case that only a few number of labeled data is available. To this end, it first learns the latent vectors of users and items for each domain so that their interactions are represented by the distances, then trains a cross-domain mapping function to encode such distance information by exploiting both overlapping users as labeled data and all the items as unlabeled data. In addition, SSCDR adopts an effective inference technique that predicts the latent vectors of cold-start users by aggregating their neighborhood information. Our extensive experiments on different CDR scenarios show that SSCDR outperforms the state-of-the-art methods in terms of CDR accuracy, particularly in the realistic settings that a small portion of users overlap between two domains.
N2N: Network Derivative Mining
Authors: Jian Kang, Hanghang Tong
Venue: CIKM
Year: 2019

Network mining plays a pivotal role in many high-impact application domains, including information retrieval, healthcare, social network analysis, security and recommender systems. State-of-the-art offers a wealth of sophisticated network mining algorithms, many of which have been widely adopted in real-world with superior empirical performance. Nonetheless, they often lack effective and efficient ways to characterize how the results of a given mining task relate to the underlying network structure. In this paper, we introduce network derivative mining problem. Given the input network and a specific mining algorithm, network derivative mining finds a derivative network whose edges measure the influence of the corresponding edges of the input network on the mining results. We envision that network derivative mining could be beneficial in a variety of scenarios, ranging from explainable network mining, adversarial network mining, sensitivity analysis on network structure, active learning, learning with side information to counterfactual learning on networks. We propose a generic framework for network derivative mining from the optimization perspective and provide various instantiations for three classic network mining tasks, including ranking, clustering, and matrix completion. For each mining task, we develop effective algorithm for constructing the derivative network based on influence function analysis, with numerous optimizations to ensure a linear complexity in both time and space. Extensive experimental evaluation on real-world datasets demonstrates the efficacy of the proposed framework and algorithms.
Industry Specific Word Embedding and its Application in Log Classification
Authors: Elham Khabiri, Wesley M. Gifford, Bhanukiran Vinzamuri, Dhaval Patel, Pietro Mazzoleni
Venue: CIKM
Year: 2019

Word, sentence and document embeddings have become the cornerstone of most natural language processing-based solutions. The training of an effective embedding depends on a large corpus of relevant documents. However, such corpus is not always available, especially for specialized heavy industries such as oil, mining, or steel. To address the problem, this paper proposes a semi-supervised learning framework to create document corpus and embedding starting from an industry taxonomy, along with a very limited set of relevant positive and negative documents. Our solution organizes candidate documents into a graph and adopts different explore and exploit strategies to iteratively create the corpus and its embedding. At each iteration, two metrics, called Coverage and Context Similarity, are used as proxy to measure the quality of the results. Our experiments demonstrate how an embedding created by our solution is more effective than the one created by processing thousands of industry-specific document pages. We also explore using our embedding in downstream tasks, such as building an industry specific classification model given labeled training data, as well as classifying unlabeled documents according to industry taxonomy terms.
Hybrid Deep Pairwise Classification for Author Name Disambiguation
Authors: Kunho Kim, Shaurya Rohatgi, C. Lee Giles
Venue: CIKM
Year: 2019

Author name disambiguation (AND) can be defined as the problem of clustering together unique authors from all author mentions that have been extracted from publication or related records in digital libraries or other sources. Pairwise classification is an essential part of AND, and is used to estimate the probability that any pair of author mentions belong to the same author. Previous studies trained classifiers with features manually extracted from each attribute of the data. Recently, others trained a model to learn a vector representation from text without considering any structure information. Both of these approaches have advantages. The former method takes advantage of the structure of data, while the latter takes into account the textual similarity across attributes. Here, we introduce a hybrid method which takes advantage of both approaches by extracting both structure-aware features and global features. In addition, we introduce a novel way to train a global model utilizing a large number of negative samples. Results on AMiner and PubMed data shows the relative improvement of the mean average precision (MAP) by more than 7.45% when compared to previous state-of-the-art methods.
Deep Learning for Blast Furnaces: Skip-Dense Layers Deep Learning Model to Predict the Remaining Time to Close Tap-holes for Blast Furnaces
Authors: Keeyoung Kim, Byeongrak Seo, Sang-Hoon Rhee, Seungmoon Lee, Simon S. Woo
Venue: CIKM
Year: 2019

Manufacturing steel requires extremely challenging industrial processes. In particular, predicting the exact time instance of opening and closing tap-holes in a blast furnace has a great influence on steel production efficiency and operating cost, in addition to human safety. However, currently predicting the time to open and close tap-holes of the blast furnace still highly relies on manual human expertise and labor. Also, most of the prior research is limited to indirectly model the level of liquids in the hearth, using complex mathematical models or classical machine learning approaches. In this paper, we use a data-driven deep learning method to more accurately predict the remaining time to close each tap-hole in a blast furnace and develop an AI-enabled automated advisory system to reduce manual human efforts as well as operation cost. We develop a multivariate time series forecasting algorithm using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models to more accurately predict the opening and closing time for the Pohang Iron and Steel Company (POSCO) blast furnace. In particular, we use and validate data from one of the largest operating furnaces in the world to develop our system. Our proposed Skip-dense CNN (S-CNN) model achieves more than 90% accuracy within ±30 minutes tolerance, compared to other LSTM baseline models. Our S-CNN model has been successfully deployed at a large-scale blast furnace of POSCO since January 2018 and has achieved similar accuracy. And we even exceeded the reported human performance in a real operational environment.
A Modular Adversarial Approach to Social Recommendation
Authors: Adit Krishnan, Hari Cheruvu, Tao Cheng, Hari Sundaram
Venue: CIKM
Year: 2019

This paper proposes a novel framework to incorporate social regularization for item recommendation. Social regularization grounded in ideas of homophily and influence appears to capture latent user preferences. However, there are two key challenges: first, the importance of a specific social link depends on the context and second, a fundamental result states that we cannot disentangle homophily and influence from observational data to determine the effect of social inference. Thus we view the attribution problem as inherently adversarial where we examine two competing hypothesis---social influence and latent interests---to explain each purchase decision. We make two contributions. First, we propose a modular, adversarial framework that decouples the architectural choices for the recommender and social representation models, for social regularization. Second, we overcome degenerate solutions through an intuitive contextual weighting strategy, that supports an expressive attribution, to ensure informative social associations play a larger role in regularizing the learned user interest space. Our results indicate significant gains (5-10% relative Recall@K) over state-of-the-art baselines across multiple publicly available datasets.
Analysis of Adaptive Training for Learning to Rank in Information Retrieval
Authors: Saar Kuzi, Sahiti Labhishetty, Shubhra Kanti Karmaker Santu, Prasad Pradip Joshi, ChengXiang Zhai
Venue: CIKM
Year: 2019

Learning to Rank is an important framework used in search engines to optimize the combination of multiple features in a single ranking function. In the existing work on learning to rank, such a ranking function is often trained on a large set of different queries to optimize the overall performance on all of them. However, the optimal parameters to combine those features are generally query-dependent, making such a strategy of "one size fits all" non-optimal. Some previous works have addressed this problem by suggesting a query-level adaptive training for learning to rank with promising results. However, previous work has not analyzed the reasons for the improvement. In this paper, we present a Best-Feature Calibration (BFC) strategy for analyzing learning to rank models and use this strategy to examine the benefit of query-level adaptive training. Our results show that the benefit of adaptive training mainly lies in the improvement of the robustness of learning to rank in cases where it does not perform as well as the best single feature.
Approximation Algorithms for Coordinating Ad Campaigns on Social Networks
Authors: Kartik Lakhotia, David Kempe
Venue: CIKM
Year: 2019

We study a natural model of coordinated social ad campaigns over a social network, based on models of Datta et al. and Aslay et al. Multiple advertisers are willing to pay the host - up to a known budget - per user exposure, whether that exposure is sponsored or organic (i.e., shared by a friend). Campaigns are seeded with sponsored ads to some users, but no network user must be exposed to too many sponsored ads. As a result, while ad campaigns proceed independently over the network, they need to be carefully coordinated with respect to their seed sets. We study the objective of maximizing the network's total ad revenue. Our main result is to show that under a broad class of social influence models, the problem can be reduced to maximizing a submodular function subject to two matroid constraints; it can therefore be approximated within a factor essentially 1/2 in polynomial time. When there is no bound on the individual seed set sizes of advertisers, the constraints correspond only to a single matroid, and the guarantee can be improved to 1 - 1/e; in that case, a factor 1/2 is achieved by a practical greedy algorithm. The 1 - 1/e approximation algorithm for the matroid-constrained problem is far from practical; however, we show that specifically under the Independent Cascade model, LP rounding and Reverse Reachability techniques can be combined to obtain a 1 - 1/e approximation algorithm which scales to several tens of thousands of nodes. Our theoretical results are complemented by experiments evaluating the extent to which the coordination of multiple ad campaigns inhibits the revenue obtained from each individual campaign, as a function of the similarity of the influence networks and the strength of ties in the network. Our experiments suggest that as networks for different advertisers become less similar, the harmful effect of competition decreases. With respect to tie strengths, we show that the most harm is done in an intermediate range.
Graph Convolutional Networks with Motif-based Attention
Authors: John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, Anup Rao
Venue: CIKM
Year: 2019

The success of deep convolutional neural networks in the domains of computer vision and speech recognition has led researchers to investigate generalizations of the said architecture to graph-structured data. A recently-proposed method called Graph Convolutional Networks has been able to achieve state-of-the-art results in the task of node classification. However, since the proposed method relies on localized first-order approximations of spectral graph convolutions, it is unable to capture higher-order interactions between nodes in the graph. In this work, we propose a motif-based graph attention model, called Motif Convolutional Networks, which generalizes past approaches by using weighted multi-hop motif adjacency matrices to capture higher-order neighborhoods. A novel attention mechanism is used to allow each individual node to select the most relevant neighborhood to apply its filter. We evaluate our approach on graphs from different domains (social networks and bioinformatics) with results showing that it is able to outperform a set of competitive baselines on the semi-supervised node classification task. Additional results demonstrate the usefulness of attention, showing that different higher-order neighborhoods are prioritized by different kinds of nodes.
Multi-scale Trajectory Clustering to Identify Corridors in Mobile Networks
Authors: Li Li, Sarah M. Erfani, Chien Aun Chan, Christopher Leckie
Venue: CIKM
Year: 2019

Deployment and management of large-scale mobile edge computing infrastructure in 5G networks has created a major challenge for mobile operators. The ability to extract common users' trajectories (i.e., corridors) in mobile networks helps mobile operators to better manage and orchestrate the allocation of network resources. However, compared with other types of trajectories, mobile trajectories are coarse, and their granularity varies due to the inconsistent density of cell towers. To identify the underlying geographical corridors of users in mobile networks, we propose a hierarchical multi-scale trajectory clustering algorithm for corridor identification by analyzing the non-homogeneity of the spatial distribution of cell towers and users' movements. To measure trajectory similarity on different scales we propose a distance measure based on Hausdorff distance that considers the cell density distribution. Common corridors are represented as weighted graphs as the final results, which can not only highlight users' frequent paths but also users' movement pattern between cell towers. The proposed method is validated using real-life datasets provided by China Mobile. Results show that by considering the heterogeneity of mobile networks, our method can achieve the best performance with more than 10% improvement in clustering quality compared with state-of-the-art algorithms.