OpenRaiser/Intern-Atlas huggingface dataset:
Tweet Geolocation: Leveraging Location, User and Peer Signals
Authors: Wen-Haw Chong, Ee-Peng Lim
Venue: CIKM
Year: 2017

Which venue is a tweet posted from? We referred this as fine-grained geolocation. To solve this problem effectively, we develop novel techniques to exploit each posting user's content history. This is motivated by our finding that most users do not share their visitation history, but have ample content history from tweet posts. We formulate fine-grained geolocation as a ranking problem whereby given a test tweet, we rank candidate venues. We propose several models that leverage on three types of signals from locations, users and peers. Firstly, the location signals are words that are indicative of venues. We propose a location-indicative weighting scheme to capture this. Next we exploit user signals from each user's content history to enrich the very limited content of their tweets which have been targeted for geolocation. The intuition is that the user's other tweets may have been from the test venue or related venues, thus providing informative words. In this regard, we propose query expansion as the enrichment approach. Finally, we exploit the signals from peer users who have similar content history and thus potentially similar visitation behavior as the users of the test tweets. This suggests collaborative filtering where visitation information is propagated via content similarities. We proposed several models incorporating different combinations of the three signals. Our experiments show that the best model incorporates all three signals. It performs 6% to 40% better than the baselines depending on the metric and dataset.
Recommendation with Capacity Constraints
Authors: Konstantina Christakopoulou, Jaya Kawale, Arindam Banerjee
Venue: CIKM
Year: 2017

In many recommendation settings, the candidate items for recommendation are associated with a maximum capacity, i.e., number of seats in a Point-of-Interest (POI) or number of item copies in the inventory. However, despite the prevalence of the capacity constraint in the recommendation process, the existing recommendation methods are not designed to optimize for respecting such a constraint. Towards closing this gap, we propose Recommendation with Capacity Constraints -- a framework that optimizes for both recommendation accuracy and expected item usage that respects the capacity constraints. We show how to apply our method to three state-of-the-art latent factor recommendation models: probabilistic matrix factorization (PMF), bayesian personalized ranking (BPR) for item recommendation, and geographical matrix factorization (GeoMF) for POI recommendation. Our experiments indicate that our framework is effective for providing good recommendations while taking the limited resources into consideration. Interestingly, our methods are shown in some cases to further improve the top-N recommendation quality of the respective unconstrained models.
Intent Based Relevance Estimation from Click Logs
Authors: Prakash Mandayam Comar, Srinivasan H. Sengamedu
Venue: CIKM
Year: 2017

Estimating the relevance of documents based on the user feedback is an essential component of search, retrieval and ranking problems. User click modeling in search has focused primarily on factoring out the position bias. It is easy to see that the query type (generic queries vs specific queries) and user intent (purchase vs exploration) also introduce a bias in the click signal. In other words, the results not matching with the user intent will not be clicked. In this paper, we outline a technique to model the interplay of query, user intent and position bias with respect to the relevance of the retrieved search results. In particular, we define two intents namely purchase and explore, and estimate the relevance of the documents with respect to these two intents. We also relate them to the relevance estimates from considering only the position bias. We empirically demonstrate the effectiveness of the proposed approach by comparing its performance against the well-known CoEC measure and the recently proposed factor model approach for relevance estimation.
Fast Word Recognition for Noise channel-based Models in Scenarios with Noise Specific Domain Knowledge
Authors: Marco Cristo, Raíza Hanada, André Luiz da Costa Carvalho, Fernando Anglada Lores, Maria da Graça Campos Pimentel
Venue: CIKM
Year: 2017

Word recognition is a challenging task faced by many applications, specially in very noisy scenarios. This problem is usually seen as the transmission of a word through a noisy-channel, such that it is necessary to determine which known word of a lexicon is the received string. To be feasible, just a reduced set of candidate words are selected. They are usually chosen if they can be transformed into the input string by applying up to k character edit operations. To rank the candidates, the most effective estimates use domain knowledge about noise sources and error distributions, extracted from real use data. In scenarios with much noise, however, such estimates, and the index strategies normally required, do not scale well as they grow exponentially with k and the lexicon size. In this work, we propose very efficient methods for word recognition in very noisy scenarios which support effective edit-based distance algorithms in a Mor-Fraenkel index, searchable using a minimum perfect hashing. The method allows the early processing of most promising candidates, such that fast pruned searches present negligible loss in word ranking quality. We also propose a linear heuristic for estimating edit-based distances which take advantage of information already provided by the index. Our methods achieve precision similar to a state-of-the-art approach, being about ten times faster.
Text Coherence Analysis Based on Deep Neural Network
Authors: Baiyun Cui, Yingming Li, Yaqing Zhang, Zhongfei Zhang
Venue: CIKM
Year: 2017

In this paper, we propose a novel deep coherence model (DCM) using a convolutional neural network architecture to capture the text coherence. The text coherence problem is investigated with a new perspective of learning sentence distributional representation and text coherence modeling simultaneously. In particular, the model captures the interactions between sentences by computing the similarities of their distributional representations. Further, it can be easily trained in an end-to-end fashion. The proposed model is evaluated on a standard Sentence Ordering task. The experimental results demonstrate its effectiveness and promise in coherence assessment showing a significant improvement over the state-of-the-art by a wide margin.
Unsupervised Matrix-valued Kernel Learning For One Class Classification
Authors: Shaobo Dang, Xiongcai Cai, Yang Wang, Jianjia Zhang, Fang Chen
Venue: CIKM
Year: 2017

This paper is concerned with the one class classification(OCC) problem. By introducing the vector-valued function with regularizations in Y-valued Reproducing Hilbert Kernel Space(RHKS), we build an unsupervised classifier and discover the outliers and inliers simultaneously. Manifold regularization is employed to preserve the local similarity of data in input space. Experimental results of the proposed and comparing methods on OCC data sets demonstrate the performance of the proposed algorithm.
Collaborative Filtering as a Case-Study for Model Parallelism on Bulk Synchronous Systems
Authors: Ariyam Das, Ishan Upadhyaya, Xiangrui Meng, Ameet Talwalkar
Venue: CIKM
Year: 2017

Industrial-scale machine learning applications often train and maintain massive models that can be on the order of hundreds of millions to billions of parameters. Model parallelism thus plays a significant role to support these machine learning tasks. Recent work in this area has been dominated by parameter server architectures that follow an asynchronous computation model, introducing added complexity and approximation in order to scale to massive workloads. In this work, we explore model parallelism in the distributed bulk-synchronous parallel (BSP) setting, leveraging some recent progress made in the area of high performance computing, in order to address these complexity and approximation issues. Using collaborative filtering as a case-study, we introduce an efficient model parallel industrial scale algorithm for alternating least squares (ALS), along with a highly optimized implementation of ALS that serves as the default implementation in MLlib, Apache Spark's machine learning library. Our extensive empirical evaluation demonstrates that our implementation in MLlib compares favorably to the leading open-source parameter server framework, and our implementation scales to massive problems on the order of 50 billion ratings and close to 1 billion parameters.
Personalized Image Aesthetics Assessment
Authors: Xiang Deng, Chaoran Cui, Huidi Fang, Xiushan Nie, Yilong Yin
Venue: CIKM
Year: 2017

Automatically assessing image quality from an aesthetic perspective is of great interest to the high-level vision research community. Existing methods are typically non-personalized and quantify image aesthetics with a universal label. However, given the fact that aesthetics is a subjective perception, how to understand user aesthetic perceptions poses a formidable challenge to image aesthetics assessment. In this paper, we propose to model user aesthetic perceptions using a set of exemplar images from social media platforms, and realize personalized aesthetics assessment by transferring this knowledge to adapt the results of the trained generic model. In this way, image aesthetics is measured from both aspects of visual quality and user tastes. Extensive experiments on two benchmark datasets well verified the potential of our approach for personalized image aesthetics assessment.
SOPER: Discovering the Influence of Fashion and the Many Faces of User from Session Logs using Stick Breaking Process
Authors: Lucky Dhakad, Mrinal Kanti Das, Chiranjib Bhattacharyya, Samik Datta, Mihir Kale, Vivek Mehta
Venue: CIKM
Year: 2017

Recommending lifestyle articles is of immediate interest to the e-commerce industry and is beginning to attract research attention. Often followed strategies, such as recommending popular items are inadequate for this vertical because of two reasons. Firstly, users have their own personal preference over items, referred to as personal styles, which lead to the long-tail phenomenon. Secondly, each user displays multiple personas, each persona has a preference over items which could be dictated by a particular occasion, e.g. dressing for a party would be different from dressing to go to office. Recommendation in this vertical is crucially dependent on discovering styles for each of the multiple personas. There is no literature which addresses this problem.
BayDNN: Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network
Authors: Daizong Ding, Mi Zhang, Shao-Yuan Li, Jie Tang, Xiaotie Chen, Zhi-Hua Zhou
Venue: CIKM
Year: 2017

Friendship is the cornerstone to build a social network. In online social networks, statistics show that the leading reason for user to create a new friendship is due to recommendation. Thus the accuracy of recommendation matters. In this paper, we propose a Bayesian Personalized Ranking Deep Neural Network (BayDNN) model for friend recommendation in social networks. With BayDNN, we achieve significant improvement on two public datasets: Epinions and Slashdot. For example, on Epinions dataset, BayDNN significantly outperforms the state-of-the-art algorithms, with a 5% improvement on NDCG over the best baseline. The advantages of the proposed BayDNN mainly come from its underlying convolutional neural network (CNN), which offers a mechanism to extract latent deep structural feature representations of the complicated network data, and a novel Bayesian personalized ranking idea, which precisely captures the users' personal bias based on the extracted deep features. To get good parameter estimation for the neural network, we present a fine-tuned pre-training strategy for the proposed BayDNN model based on Poisson and Bernoulli probabilistic models.
Efficient Discovery of Abnormal Event Sequences in Enterprise Security Systems
Authors: Boxiang Dong, Zhengzhang Chen, Wendy Hui Wang, Lu-An Tang, Kai Zhang, Ying Lin, Zhichun Li, Haifeng Chen
Venue: CIKM
Year: 2017

Intrusion detection system (IDS) is an important part of enterprise security system architecture. In particular, anomaly-based IDS has been widely applied to detect single abnormal process events that deviate from the majority. However, intrusion activity usually consists of a series of low-level heterogeneous events. The gap between low-level process events and high-level intrusion activities makes it particularly challenging to identify process events that are truly involved in a real malicious activity, and especially considering the massive 'noisy' events filling the event sequences. Hence, the existing work that focus on detecting single events can hardly achieve high detection accuracy. In this work, we formulate a novel problem in intrusion detection - suspicious event sequence discovery, and propose GID, an efficient graph-based intrusion detection technique that can identify abnormal event sequences from massive heterogeneous process traces with high accuracy. We fully implement GID and deploy it into a real-world enterprise security system, and it greatly helps detect the advanced threats and optimize the incident response. Executing GID on both static and streaming data shows that GID is efficient (processes about 2 million records per minute) and accurate for intrusion detection.
Stream Aggregation Through Order Sampling
Authors: Nick G. Duffield, Yunhong Xu, Liangzhen Xia, Nesreen K. Ahmed, Minlan Yu
Venue: CIKM
Year: 2017

This paper introduces a new single-pass reservoir weighted-sampling stream aggregation algorithm, Priority-Based Aggregation (PBA). While order sampling is a powerful and efficient method for weighted sampling from a stream of uniquely keyed items, there is no current algorithm that realizes the benefits of order sampling in the context of stream aggregation over non-unique keys. A naive approach to order sample regardless of key then aggregate the results is hopelessly inefficient. In distinction, our proposed algorithm uses a single persistent random variable across the lifetime of each key in the cache, and maintains unbiased estimates of the key aggregates that can be queried at any point in the stream. The basic approach can be supplemented with a Sample and Hold pre-sampling stage with a sampling rate adaptation controlled by PBA. This approach represents a considerable reduction in computational complexity compared with the state of the art in adapting Sample and Hold to operate with a fixed cache size. Concerning statistical properties, we prove that PBA provides unbiased estimates of the true aggregates. We analyze the computational complexity of PBA and its variants, and provide a detailed evaluation of its accuracy on synthetic and trace data. Weighted relative error is reduced by 40% to 65% at sampling rates of 5% to 17%, relative to Adaptive Sample and Hold; there is also substantial improvement for rank queries.
On Discovering the Number of Document Topics via Conceptual Latent Space
Authors: Nghia Duong-Trung, Lars Schmidt-Thieme
Venue: CIKM
Year: 2017

Topic modeling is a widely used technique in knowledge discovery and data mining. However, finding the right number of topics in a given text source has remained a challenging issue. In this paper, we study the concept of conceptual stability via nonnegative matrix factorization. Based on this finding, we propose a method to identify the correct number of topics and offer empirical evidence in its favor in terms of classification accuracy and the number of topics that are naturally present in the text sources. Experiments on real-world text corpora demonstrate that the proposed method has outperformed state-of-the-art latent Dirichlet allocation and nonnegative matrix factorization models.
Chinese Named Entity Recognition with Character-Word Mixed Embedding
Authors: Shijia E, Yang Xiang
Venue: CIKM
Year: 2017

Named Entity Recognition (NER) is an important basis for the tasks in natural language processing such as relation extraction, entity linking and so on. The common method of existing Chinese NER systems is to use the character sequence as the input, and the intention is to avoid the word segmentation. However, the character sequence cannot express enough semantic information, so that the recognition accuracy of Chinese NER is not as good as western language such as English. To solve this issue, we propose a Chinese NER method based on Character-Word Mixed Embedding (CWME), and the method is in accord with the pipeline of Chinese natural language processing. Our experiments show that incorporating CWME can effectively improve the performance for the Chinese corpus with state-of-the-art neural architectures widely used in NER, and the proposed method yields nearly 9% absolute improvement over previously results.
SimMeme: Semantic-Based Meme Search
Authors: Maya Ekron, Tova Milo, Brit Youngmann
Venue: CIKM
Year: 2017

With the proliferation of social image-sharing applications, image search becomes an increasingly common activity. In this work, we focus on a particular class of images that convey semantic meaning beyond the visual appearance, and whose search presents particular challenges. A prominent example is Memes, an emerging popular type of captioned pictures, which we will use in this demo to demonstrate our solution. Unlike in conventional image-search, visually similar Memes may reflect different concepts. The intent is sometimes captured by user annotations, but these too are often incomplete and ambiguous. Thus, a deeper analysis of the semantic relations among Memes is required for an accurate search. To address this problem, we present SimMeme, a semantic aware search engine for Memes. SimMeme uses a generic graph-based data model that aligns all the information available about the Memes with a semantic ontology. A novel similarity measure that interweaves common image, textual, structural and semantic similarities into one holistic measure is employed to effectively answer user queries. We will demonstrate the operation of SimMeme over a large repository of real-life annotated Memes which we have constructed by web crawling and crowd annotations, allowing users to appreciate the quality of the search results as well as the execution efficiency.
UFeed: Refining Web Data Integration Based on User Feedback
Authors: Ahmed El-Roby, Ashraf Aboulnaga
Venue: CIKM
Year: 2017

One of the main challenges in large-scale data integration for relational schemas is creating an accurate mediated schema, and generating accurate semantic mappings between heterogeneous data sources and this mediated schema. Some applications can start with a moderately accurate mediated schema and mappings and refine them over time, which is referred to as the pay-as-you-go approach to data integration. Creating the mediated schema and mappings automatically to bootstrap the pay-as-you-go approach has been extensively studied. However, refining the mediated schema and mappings is still an open challenge because the data sources are usually heterogeneous and use diverse and sometimes ambiguous vocabularies. In this paper, we introduce UFeed, a system that refines relational mediated schemas and mappings based on user feedback over query answers. UFeed translates user actions into refinement operations that are applied to the mediated schema and mappings to improve their quality. We experimentally verify that UFeed improves the quality of query answers over real heterogeneous data sources extracted from the web.
An Empirical Study of Embedding Features in Learning to Rank
Authors: Faezeh Ensan, Ebrahim Bagheri, Amal Zouaq, Alexandre Kouznetsov
Venue: CIKM
Year: 2017

This paper explores the possibility of using neural embedding features for enhancing the effectiveness of ad hoc document ranking based on learning to rank models. We have extensively introduced and investigated the effectiveness of features learnt based on word and document embeddings to represent both queries and documents. We employ several learning to rank methods for document ranking using embedding-based features, keyword-based features as well as the interpolation of the embedding-based features with keyword-based features. The results show that embedding features have a synergistic impact on keyword based features and are able to provide statistically significant improvement on harder queries.
Privacy of Hidden Profiles: Utility-Preserving Profile Removal in Online Forums
Authors: Sedigheh Eslami, Asia J. Biega, Rishiraj Saha Roy, Gerhard Weikum
Venue: CIKM
Year: 2017

Users who wish to leave an online forum often do not have the freedom to erase their data completely from the service providers' (SP) system. The primary reason behind this is that analytics on such user data form a core component of many online providers' business models. On the other hand, if the profiles reside in the SP's system in an unchanged form, major privacy violations may occur if the infrastructure is compromised, or the SP is acquired by another organization. In this work, we investigate an alternative solution to standard profile removal, where posts of different users are split and merged into synthetic mediator profiles. The goal of our framework is to preserve the SP's data mining utility as far as possible, while minimizing users' privacy risks. We present several mechanisms of assigning user posts to such mediator accounts and show the effectiveness of our framework using data from StackExchange and various health forums.
QoS-Aware Scheduling of Heterogeneous Servers for Inference in Deep Neural Networks
Authors: Zhou Fang, Tong Yu, Ole J. Mengshoel, Rajesh K. Gupta
Venue: CIKM
Year: 2017

Deep neural networks (DNNs) are popular in diverse fields such as computer vision and natural language processing. DNN inference tasks are emerging as a service provided by cloud computing environments. However, cloud-hosted DNN inference faces new challenges in workload scheduling for the best Quality of Service (QoS), due to dependence on batch size, model complexity and resource allocation. This paper represents the QoS metric as a utility function of response delay and inference accuracy. We first propose a simple and effective heuristic approach that keeps low response delay and satisfies the requirement on processing throughput. Then we describe an advanced deep reinforcement learning (RL) approach that learns to schedule from experience. The RL scheduler is trained to maximize QoS, using a set of system statuses as the input to the RL policy model. Our approach performs scheduling actions only when there are free GPUs, thus reduces scheduling overhead over common RL schedulers that run at every continuous time step. We evaluate the schedulers on a simulation platform and demonstrate the advantages of RL over heuristics.