OpenRaiser/Intern-Atlas huggingface dataset:
Variational Recurrent Model for Session-based Recommendation
Authors: Zhitao Wang, Chengyao Chen, Ke Zhang, Yu Lei, Wenjie Li
Venue: CIKM
Year: 2018

Session-based recommendation performance has been significantly improved by Recurrent Neural Networks (RNN). However, existing RNN-based models do not expose the global knowledge of frequent click patterns or consider variability of sequential behaviors in sessions. In this paper, we propose a novel Variational Recurrent Model (VRM), which employs the stochastic latent variable to capture the knowledge of frequent click patterns and impose variability for the sequential behavior modeling. A stochastic generative process of session sequence is specified, where the latent variable modulates the generation of session sequences in RNN. We further extend VRM to a Conditional Variational Recurrent Model (CVRM) by considering additional information (e.g., focused category in sessions) as the generative condition. When evaluated on a public benchmark dataset, VRM and its extension clearly demonstrate their superiority over popular baselines and state-of-the-art models.
Device-Aware Rule Recommendation for the Internet of Things
Authors: Beidou Wang, Xin Guo, Martin Ester, Ziyu Guan, Bandeep Singh, Yu Zhu, Jiajun Bu, Deng Cai
Venue: CIKM
Year: 2018

With over 34 billion IoT devices to be installed by 2020, the Internet of Things (IoT) is fundamentally changing our lives. One of the greatest benefits of the IoT is the powerful automations achieved by applying rules to IoT devices. For instance, a rule named "Make me a cup of coffee when I wake up'' automatically turns on the coffee machine when the sensor in the bedroom detects motion in the morning. With large numbers of possible rules out there, a recommendation system is of great necessity to help users find rules they need. However, little effort has been made to design a model tailored for the IoT rule recommendation, which comes with lots of new challenges compared with traditional recommendation tasks. We not only need to re-define "users'' and "items'' in the recommendation task, but also have to consider a new type of entities, devices, and the extra information and constraints brought by them. To handle these challenges, we propose a novel efficient recommendation algorithm, which not only considers the implicit feedback of users on rules, but also takes user-rule-device interactions and the match between rule device requirements and user device possessions into account. In collaboration with Samsung, one of the leading companies in this field, we have designed an IoT rule recommendation framework and evaluated our algorithm on a real-life industry dataset. Experiments show the effectiveness and efficiency of our method.
Distributed Ledger Technology for Document and Workflow Management in Trade and Logistics
Authors: Ziyuan Wang, Dain Yap Liffman, Dileban Karunamoorthy, Ermyas Abebe
Venue: CIKM
Year: 2018

Logistics management plays a crucial role in the execution and success of international trades. Current solutions for logistics management suffer from a number of issues. One of the major areas for improvement is inconsistent data and lack of trust and transparency between multiple participants. Distributed Ledger Technology (DLT) or blockchain has the inherent characteristics to address this issue, and is well suited to be applied to supply chain document and workflow management. This paper demonstrates how to apply DLT to achieve a higher level of efficiency through consistent data store, automated workflow process, and tamper-proof transaction history for provenance in the supply chain.
Multi-Emotion Category Improving Embedding for Sentiment Classification
Authors: Shuo Wang, Xiaofeng Meng
Venue: CIKM
Year: 2018

Sentiment analysis and opinion mining are significant and valuable for subject information extraction from the text. Word embedding that can map the words to low-dimensional vector representations has been widely used in natural language processing tasks. But the word embedding based on context such as Word2Vec and GloVe is lack of capturing the sentiment information. Most of existing sentiment analysis methods incorporate sentiment polarity (positive and negative) to improve the sentiment embedding for sentiment tasks. Instead of making a new word embedding model, we introduce the multi-emotion category (MEC) model to improve the pre-trained word vectors which aims to move target word vectors closer to the words from both similar semantics and similar emotions. The MEC model can give eight-dimensional vector for one word in emotion space that can capture more sentiment information than the binary polarity labels. In addition, the obvious advantage of the MEC model is that it can be fit for any pre-trained word embedding. The experimental results on several Chinese and English data sets show that this new model can improve the conventional word embedding and some existing sentiment embedding for sentiment classification.
Multi-View Group Anomaly Detection
Authors: Hongtao Wang, Pan Su, Miao Zhao, Hongmei Wang, Gang Li
Venue: CIKM
Year: 2018

Multi-view anomaly detection is a challenging issue due to diverse data generation mechanisms and inconsistent cluster structures of different views. Existing methods of point anomaly detection are ineffective for scenarios where individual instances are normal, but their collective behavior as a group is abnormal. In this paper, we formalize this group anomaly detection issue, and propose a novel non-parametric bayesian model, named Multi-view Group Anomaly Detection (MGAD). By representing the multi-view data with different latent group and topic structures, MGAD first discovers the distribution of groups or topics in each view, then detects group anomalies effectively. In order to solve the proposed model, we conduct the collapsed Gibbs sampling algorithm for model inference. We evaluate our model on both synthetic and real-world datasets with different anomaly settings. The experimental results demonstrate the effectiveness of the proposed approach on detecting multi-view group anomalies.
DualBoost: Handling Missing Values with Feature Weights and Weak Classifiers that Abstain
Authors: Weihong Wang, Jie Xu, Yang Wang, Chen Cai, Fang Chen
Venue: CIKM
Year: 2018

Missing values in real world datasets are a common issue. Handling missing values is one of the most key aspects in data mining, as it can seriously impact the performance of predictive models. In this paper we proposed a unified Boosting framework that consolidates model construction and missing value handling. At each Boosting iteration, weights are assigned to both the samples and features. The sample weights make difficult samples become the learning focus, while the feature weights enable critical features to be compensated by less critical features when they are unavailable. A weak classifier that abstains (i.e, produce no prediction when required feature value is missing) is learned on a data subset determined by the feature weights. Experimental results demonstrate the efficacy and robustness of the proposed method over existing Boosting algorithms.
Empirical Evidence for Search Effectiveness Models
Authors: Alfan Farizki Wicaksono, Alistair Moffat
Venue: CIKM
Year: 2018

Given a SERP in response to a user-originated query, Moffat et al. (CIKM 2013; TOIS 2017) suggest that C(i), the conditional continuation probability of the user examining the (i+1)st element presented in the SERP, given that they are known to have examined the ith one, is positively correlated with both i and with the user's initial estimate of the volume of answer pages they are looking for, and negatively correlated with the extent to which suitable answer pages have been identified in the SERP at positions 1 through i. Here we first describe a methodology for specifying how C(i) should be defined in practical (as against ideal) settings, and then evaluate the applicability of the approach using three large search interaction logs from two different sources.
Joint Dictionary Learning and Semantic Constrained Latent Subspace Projection for Cross-Modal Retrieval
Authors: Jianlong Wu, Zhouchen Lin, Hongbin Zha
Venue: CIKM
Year: 2018

With the increasing of multi-modal data on the internet, cross-modal retrieval has received a lot of attention in recent years. It aims to use one type of data as query and retrieve results of another type. For different modality data, how to reduce their heterogeneous property and preserve their local relationship are two main challenges. In this paper, we present a novel joint dictionary learning and semantic constrained latent subspace learning method for cross-modal retrieval~(JDSLC) to deal with above two issues. In this unified framework, samples from different modalities are encoded by their corresponding dictionaries to reduce the semantic gap. In the meantime, we learn modality-specific projection matrices to map the sparse coefficients into the shared latent subspace. Meanwhile, we impose a novel cross-modal similarity constraint to make the representations of samples that belong to same class but from different modalities as close as possible in the latent subspace. An efficient algorithm is proposed to jointly optimize the proposed model and learn the optimal dictionary, coefficients and projection matrix for each modality. Extensive experimental results on multiple benchmark datasets show that our method outperforms the state-of-the-art approaches.
Imbalanced Sentiment Classification with Multi-Task Learning
Authors: Fangzhao Wu, Chuhan Wu, Junxin Liu
Venue: CIKM
Year: 2018

Supervised learning methods are widely used in sentiment classification. However, when sentiment distribution is imbalanced, the performance of these methods declines. In this paper, we propose an effective approach for imbalanced sentiment classification. In our approach, multiple balanced subsets are sampled from the imbalanced training data and a multi-task learning based framework is proposed to learn robust sentiment classifier from these subsets collaboratively. In addition, we incorporate prior knowledge of sentiment expressions extracted from both existing sentiment lexicons and massive unlabeled data into our approach to enhance the learning of sentiment classifier in imbalanced scenario. Experimental results on benchmark datasets validate the effectiveness of our approach in improving imbalanced sentiment classification.
Semi-Supervised Collaborative Learning for Social Spammer and Spam Message Detection in Microblogging
Authors: Fangzhao Wu, Chuhan Wu, Junxin Liu
Venue: CIKM
Year: 2018

It is important to detect social spammers and spam messages in microblogging platforms. Existing methods usually handle the detection of social spammers and spam messages as two separate tasks using supervised learning techniques. However, labeled samples are usually scarce and manual annotation is expensive. In this paper, we propose a semi-supervised collaborative learning approach to jointly detect social spammers and spam messages in microblogging platforms. In our approach, the social spammer classifier and spam message classifier are collaboratively trained by exploiting the inherent relatedness between these tasks. In addition, unlabeled samples are incorporated into model training with the help of social contexts of users and messages. Experiments on real-world dataset show our approach can effectively improve the performance of both social spammer detection and spam message detection.
A Quest for Structure: Jointly Learning the Graph Structure and Semi-Supervised Classification
Authors: Xuan Wu, Lingxiao Zhao, Leman Akoglu
Venue: CIKM
Year: 2018

Semi-supervised learning (SSL) is effectively used for numerous classification problems, thanks to its ability to make use of abundant unlabeled data. The main assumption of various SSL algorithms is that the nearby points on the data manifold are likely to share a label. Graph-based SSL constructs a graph from point-cloud data as an approximation to the underlying manifold, followed by label inference. It is no surprise that the quality of the constructed graph in capturing the essential structure of the data is critical to the accuracy of the subsequent inference step [6]. How should one construct a graph from the input point-cloud data for graph-based SSL? In this work we introduce a new, parallel graph learning framework (called PG-learn) for the graph construction step of SSL. Our solution has two main ingredients: (1) a gradient-based optimization of the edge weights (more specifically, different kernel bandwidths in each dimension) based on a validation loss function, and (2) a parallel hyperparameter search algorithm with an adaptive resource allocation scheme. In essence, (1) allows us to search around a (random) initial hyperparameter configuration for a better one with lower validation loss. Since the search space of hyperparameters is huge for high-dimensional problems, (2) empowers our gradient-based search to go through as many different initial configurations as possible, where runs for relatively unpromising starting configurations are terminated early to allocate the time for others. As such, PG-learn is a carefully-designed hybrid of random and adaptive search. Through experiments on multi-class classification problems, we show that PG-learn significantly outperforms a variety of existing graph construction schemes in accuracy (per fixed time budget for hyperparameter tuning), and scales more effectively to high dimensional problems.
Modeling Consumer Buying Decision for Recommendation Based on Multi-Task Deep Learning
Authors: Qiaolin Xia, Peng Jiang, Fei Sun, Yi Zhang, Xiaobo Wang, Zhifang Sui
Venue: CIKM
Year: 2018

Although marketing researchers and sociologists have recognized the importance of buying decision process and its significant influence on consumer's purchasing behaviors, existing recommender systems do not explicitly model the consumer buying decision process or capture the sequential regularities of what happens before and after each purchase. In this paper, we try to bridge the gap and improve recommendation systems by explicitly modeling consumer buying decision process and corresponding stages. In particular, we propose a multi-task learning model with long short-term memory networks (LSTM) to learn consumer buying decision process. It maps items, users, product categories, and the behavior sequences into real valued vectors, with which the probability of purchasing a product can be estimated. In this way, the model can capture user intentions and preferences, predicts the conversion rate of each candidate product, and makes recommendations accordingly. Experiments on real world data demonstrate the effectiveness of the proposed approach.
Neighborhood Voting: A Novel Search Scheme for Hashing
Authors: Yan Xiao, Jiafeng Guo, Yanyan Lan, Jun Xu, Xueqi Cheng
Venue: CIKM
Year: 2018

Hashing techniques for approximate nearest neighbor search (ANNS) encode data points into a set of short binary codes, while trying to preserve the neighborhood structure of the original data as much as possible. With the binary codes, the task of ANNS can be easily conducted over large-scale dataset, due to the high efficiency of pairwise comparison with the Hamming distance. Although binary codes have low computation and storage cost, the data are heavily compressed so that partial neighborhood structure information would be inevitably lost. To address this issue, we propose to introduce the k-nearest neighbors (k-NNs) in the original space into the Hamming space (i.e., associating a binary code with its original k-NNs) to enhance the effectiveness of existing hashing techniques with little overhead. Based on this idea, we develop a novel search scheme for hashing techniques namely neighborhood voting, i.e., each point retrieved by a query code will vote for its neighbors and itself, and the more voted, the better candidates. In this way, search in hashing is not simply the collision between codes (i.e., query code and candidate code), but also the collision between neighbors (i.e., neighbors of candidate points). The underlying assumption is that the true neighbors of a query point should be close to each other, while points with similar binary codes but seldom be the neighbors of other candidate points would be false positives. We introduce a novel data structure called aggregated hash table for implementing our idea and accelerating the online search process. Experimental results show that our search scheme can significantly improve the search effectiveness while having good efficiency over different existing hashing techniques.
Semi-Supervised Multi-Label Feature Selection by Preserving Feature-Label Space Consistency
Authors: Yuanyuan Xu, Jun Wang, Shuai An, Jinmao Wei, Jianhua Ruan
Venue: CIKM
Year: 2018

Semi-supervised learning and multi-label learning pose different challenges for feature selection, which is one of the core techniques for dimension reduction, and the exploration of reducing feature space for multi-label learning with incomplete label information is far from satisfactory. Existing feature selection approaches devote attention to either of two issues, namely, alleviating negative effects of imperfectly predicted labels and quantitatively evaluating label correlations, exclusively for semi-supervised or multi-label scenarios. A unified framework to extract label correlation information with incomplete prior knowledge and embed this information in feature selection however, is rarely touched. In this paper, we propose a space consistency-based feature selection model to address this issue. Specifically, correlation information in feature space is learned based on the probabilistic neighborhood similarities, and correlation information in label space is optimized by preserving feature-label space consistency. This mechanism contributes to appropriately extracting label information in semi-supervised multi-label learning scenario and effectively employing this information to select discriminative features. An extensive experimental evaluation on real-world data shows the superiority of the proposed approach under various evaluation metrics.
METIC: Multi-Instance Entity Typing from Corpus
Authors: Bo Xu, Zheng Luo, Luyang Huang, Bin Liang, Yanghua Xiao, Deqing Yang, Wei Wang
Venue: CIKM
Year: 2018

This paper addresses the problem ofmulti-instance entity typing from corpus. Current approaches mainly rely on the structured features (\textitattributes, attribute-value pairs andtags ) of the entities. However, their effectiveness is largely dependent on the completeness of structured features, which unfortunately is not guaranteed in KBs. In this paper, we therefore propose to use the text corpus of an entity to infer its types, and propose a multi-instance method to tackle this problem. We take each mention of an entity in KBs as an instance of the entity, and learn the types of these entities from multiple instances. Specifically, we first use an end-to-end neural network model to type each instance of an entity, and then use an integer linear programming (ILP) method to aggregate the predicted type results from multiple instances. Experimental results show the effectiveness of our method.
A Network-embedding Based Method for Author Disambiguation
Authors: Jun Xu, Siqi Shen, Dongsheng Li, Yongquan Fu
Venue: CIKM
Year: 2018

Most existing author disambiguation work relies heavily on feature engineering or cannot use multiple paper relationships. In this work, we propose a network-embedding based method for author disambiguation. For each ambiguous name, we construct networks among papers sharing an ambiguous name, and connect papers with multiple relationships (e.g., co-authoring a paper). We focus on maximizing the gap between positive paper edges and negative edges, and propose a graph coarsening technique to learn global information. Further, we design a clustering algorithm which partitions paper representations into disjoint sets such that each set contains all papers of a unique author. Through extensive experiments, we show that our method is significantly better than the state-of-the-art author disambiguation and network-embedding methods.
Exploring a High-quality Outlying Feature Value Set for Noise-Resilient Outlier Detection in Categorical Data
Authors: Hongzuo Xu, Yongjun Wang, Li Cheng, Yijie Wang, Xingkong Ma
Venue: CIKM
Year: 2018

Unavoidable noise in real-world categorical data presents significant challenges to existing outlier detection methods because they normally fail to separate noisy values from outlying values. Feature subspace-based methods inevitably mix noisy values when retaining an entire feature because a feature may contain both outlying values and noisy values. Pattern-based methods are normally based on frequency and are easily misled by noisy values, resulting in many faulty patterns. This paper introduces a novel unsupervised framework termed OUVAS, and its parameter-free instantiation RHAC to explore a high-quality outlying value set for detecting outliers in noisy categorical data. Based on the observation that the relations between values reflect their essence, OUVAS investigates value similarities to cluster values into different groups and combines cluster-level analysis and value-level refinement to identify an outlying value set. RHAC instantiates OUVAS by three successive modules (i.e., the combination of Ochiai coefficient and LOUVAIN algorithm to cluster values, hierarchical value coupling learning to perform cluster-level analysis, and a threshold to divide fake and real outlying values in value-level refinement). We show that (i) RHAC-based outlier detector significantly outperforms five state-of-the-art outlier detection methods; (ii) Extended RHAC-based feature selection method successfully improves the performance of existing outlier detectors and performs better than two latest outlying feature selection methods.
Exploring People's Attitudes and Behaviors Toward Careful Information Seeking in Web Search
Authors: Takehiro Yamamoto, Yusuke Yamamoto, Sumio Fujita
Venue: CIKM
Year: 2018

This study investigates how people carefully search for the Web to obtain credible and accurate information. The goal of this study is to better understand people's attitudes toward careful information seeking via Web search, and the relationship between such attitudes and their daily search behaviors. To this end, we conducted two experiments. We first administrated an online questionnaire to investigate how people's attitudes toward using the strategies for verifying information in the Web search process differ based on various factors such as their credulity toward Web information, individual thinking styles, educational background, and search expertise. We then analyzed their one-year and one-month query logs of a commercial Web search engine to explore how their daily search behaviors are different according to their attitudes. The analysis of the questionnaire and the query logs obtained from ¥subjects participants revealed that (i) the people's attitudes toward using the verification strategies in Web search are positively correlated to their Need for Cognition (NFC), educational background, and search expertise; (ii) people with strong attitudes are likely to click lower-ranked search results than those with intermediate levels of attitude; (iii) people with strong attitudes are more likely to use the terms such as "evidence'' or "truth'' in their queries, possibly to scrutinize the uncertain or incredible information; and (iv) the behavioral differences found in (ii) and (iii) are not identified from the differences in the participants' educational backgrounds. These findings help us explore future directions for a new Web search system that encourages people to be more careful in Web search, and suggest the need for an educational program or training to facilitate the attitudes and skills for using Web search engines to obtain accurate information.