OpenRaiser/Intern-Atlas huggingface dataset:
Where to Place Your Next Restaurant?: Optimal Restaurant Placement via Leveraging User-Generated Reviews
Authors: Feng Wang, Li Chen, Weike Pan
Venue: CIKM
Year: 2016

When opening a new restaurant, geographical placement is of prime importance in determining whether it will thrive. Although some methods have been developed to assess the attractiveness of candidate locations for a restaurant, the accuracy is limited as they mainly rely on traditional data sources, such as demographic studies or consumer surveys. With the advent of abundant user-generated restaurant reviews, there is a potential to leverage these reviews to gain some insights into users' preferences for restaurants. In this paper, we particularly take advantage of user-generated reviews to construct predictive features for assessing the attractiveness of candidate locations to expand a restaurant. Specifically, we investigate three types of features: review-based market attractiveness, review-based market competitiveness and geographic characteristics of a location under consideration for a prospective restaurant. We devise the three sets of features and incorporate them into a regression model to predict the number of check-ins that a prospective restaurant at a candidate location would be likely to attract. We then conduct an experiment with real-world restaurant data, which demonstrates the predictive power of features we constructed in this paper. Moreover, our experimental results suggest that market attractiveness and market competitiveness features mined solely from user-generated restaurant reviews are more predictive than geographic features.
Incremental Mining of High Utility Sequential Patterns in Incremental Databases
Authors: Jun-Zhe Wang, Jiun-Long Huang
Venue: CIKM
Year: 2016

High utility sequential pattern (HUSP) mining is an emerging topic in pattern mining, and only a few algorithms have been proposed to address it. In practice, most sequence databases usually grow over time, and it is inefficient for existing algorithms to mine HUSPs from scratch when databases grow with a small portion of updates. In view of this, we propose the IncUSP-Miner algorithm to mine HUSPs incrementally. Specifically, to avoid redundant computations, we propose a tighter upper bound of the utility of a sequence, called TSU, and then design a novel data structure, called the candidate pattern tree, to maintain the sequences whose TSU values are greater than or equal to the minimum utility threshold. Accordingly, to avoid keeping a huge amount of utility information for each sequence, a set of auxiliary utility information is designed to be stored in each tree node. Moreover, for those nodes whose utilities have to be updated, a strategy is also proposed to reduce the amount of computation, thereby improving the mining efficiency. Experimental results on three real datasets show that IncUSP-Miner is able to efficiently mine HUSPs incrementally.
Learning to Extract Conditional Knowledge for Question Answering using Dialogue
Authors: Pengwei Wang, Lei Ji, Jun Yan, Lianwen Jin, Wei-Ying Ma
Venue: CIKM
Year: 2016

Knowledge based question answering (KBQA) has attracted much attention from both academia and industry in the field of Artificial Intelligence. However, many existing knowledge bases (KBs) are built by static triples. It is hard to answer user questions with different conditions, which will lead to significant answer variances in questions with similar intent. In this work, we propose to extract conditional knowledge base (CKB) from user question-answer pairs for answering user questions with different conditions through dialogue. Given a subject, we first learn user question patterns and conditions. Then we propose an embedding based co-clustering algorithm to simultaneously group the patterns and conditions by leveraging the answers as supervisor information. After that, we extract the answers to questions conditioned on both question pattern clusters and condition clusters as a CKB. As a result, when users ask a question without clearly specifying the conditions, we use dialogues in natural language to chat with users for question specification and answer retrieval. Experiments on real question answering (QA) data show that the dialogue model using automatically extracted CKB can more accurately answer user questions and significantly improve user satisfaction for questions with missing conditions.
Webpage Depth-level Dwell Time Prediction
Authors: Chong Wang, Achir Kalra, Cristian Borcea, Yi Chen
Venue: CIKM
Year: 2016

The amount of time spent by users at specific page depths within webpages, called dwell time, can be used by web publishers to decide where to place online ads and what type of ads to place at different depths within a webpage. This paper presents a model to predict the dwell time for a given "user, webpage, depth" triplet based on historic data collected by publishers. Dwell time prediction is difficult due to user behavior variability and data sparsity. We adopt the Factorization Machines model because it is able to capture the interaction between users and webpages, overcome the data sparsity issue, and provide flexibility to add auxiliary information such as the visible area of a user's browser. Experimental results using data from a large web publisher demonstrate that our model outperforms deterministic and regression-based comparison models.
Improving Personalized Trip Recommendation by Avoiding Crowds
Authors: Xiaoting Wang, Christopher Leckie, Jeffrey Chan, Kwan Hui Lim, Tharshan Vaithianathan
Venue: CIKM
Year: 2016

There has been a growing interest in recommending trips for tourists using location-based social networks. The challenge of trip recommendation not only lies in searching for relevant points-of-interest (POIs) to form a personalized trip, but also selecting the best time of day to visit the POIs. Popular POIs can be too crowded during peak times, resulting in long queues and delays. In this work, we propose the Personalized Crowd-aware Trip Recommendation (PersCT) algorithm to recommend personalized trips that also avoid the most crowded times of the POIs. We model the problem as an extension of the Orienteering Problem with multiple constraints. We extract user interests by collaborative filtering and we propose an extension of the Ant Colony Optimisation algorithm to merge user interests with POI popularity and crowdedness data to recommend trips. We evaluate our algorithm using foot traffic information obtained from a real-life pedestrian sensor dataset and user travel histories extracted from a Flickr photo dataset. We show that our algorithm out-performs several benchmarks in achieving a balance between conflicting objectives by satisfying user interests while reducing the crowdedness of the trips.
Improving Advertisement Recommendation by Enriching User Browser Cookie Attributes
Authors: Liang Wang, Kuang-chih Lee, Quan Lu
Venue: CIKM
Year: 2016

User attributes including online behavior history and demographic information are the keys to decide whether a user is the right audience for an advertisement. When a user visits a website, the website generally plugs a browser cookie string (`bcookie' for short). The bcookie is then used as an identifier to collect the user's online behavior, as well as the joint key to link user profile attributes, such as demographic information and browsing history. However, the same users can have different bcookies across different browsers and devices. Moreover, bcookies can expire after some period, be cleared by browsers or users. This situation of bcookie discounting typically introduces both performance and delivery problems in online advertising since advertisers are hard to find the most receptive audiences based on the user profile information. In this paper, we try to tackle this problem by using an `assistant identifier' to find the linkage between different bcookies. For most of the Internet company, in addition to the bcookie information, there are always other identifiers such as IP address, user agent, OS type and version, etc., stored in the serving log data. Therefore, we propose an unified framework to link different bcookies from the same users according to those assistant identifiers. Specifically, our proposed method first constructs a bipartite graph with linkages between the assistant identifiers and the bcookies. Next all attributes associated with each bcookie are propagated along the graph using the state-of-the-art random walk model. Offline comparative experimental studies are conducted to confirm that by enriching the bcookie attributes we can recover 20% more online users whose bcookie information is lost, which is greatly helpful to delivery more budget spending with a little loss in precision of predicting converted users. On-product evaluation further confirms the effectiveness of the proposed method.
OrientStream: A Framework for Dynamic Resource Allocation in Distributed Data Stream Management Systems
Authors: Chunkai Wang, Xiaofeng Meng, Qi Guo, Zujian Weng, Chen Yang
Venue: CIKM
Year: 2016

Distributed data stream management systems (DDSMS) are usually composed of upper layer relational query systems (RQS) and lower layer stream processing systems (SPS). When users submit new queries to RQS, a query planner needs to be converted into a directed acyclic graph (DAG) consisting of tasks which are running on SPS. Based on different query requests and data stream properties, SPS need to configure different deployments strategies. However, how to dynamically predict deployment configurations of SPS to ensure the processing throughput and low resource usage is a great challenge. This article presents OrientStream, a framework for dynamic resource allocation in DDSMS using incremental machine learning techniques. By introducing the data-level, query plan-level, operator-level and cluster-level's four-level feature extraction mechanism, we firstly use the different query workloads as training sets to predict the resource usage of DDSMS and then select the optimal resource configuration from candidate settings based on the current query requests and stream properties. Finally, we validate our approach on the open source SPS--Storm. Experiments show that OrientStream can reduce CPU usage of 8%-15% and memory usage of 38%-48% respectively.
CRISP: Consensus Regularized Selection based Prediction
Authors: Ping Wang, Karthik K. Padthe, Bhanukiran Vinzamuri, Chandan K. Reddy
Venue: CIKM
Year: 2016

Integrating regularization methods with standard loss functions such as the least squares, hinge loss, etc., within a regression framework has become a popular choice for researchers to learn predictive models with lower variance and better generalization ability. Regularizers also aid in building interpretable models with high-dimensional data which makes them very appealing. It is observed that each regularizer is uniquely formulated in order to capture data-specific properties such as correlation, structured sparsity and temporal smoothness. The problem of obtaining a consensus among such diverse regularizers while learning a predictive model is extremely important in order to determine the optimal regularizer for the problem. The advantage of such an approach is that it preserves the simplicity of the final model learned by selecting a single candidate model which is not the case with ensemble methods as they use multiple candidate models for prediction. This is called the consensus regularization problem which has not received much attention in the literature due to the inherent difficulty associated with learning and selecting a model from an integrated regularization framework. To solve this problem, in this paper, we propose a method to generate a committee of non-convex regularized linear regression models, and use a consensus criterion to determine the optimal model for prediction. Each corresponding non-convex optimization problem in the committee is solved efficiently using the cyclic-coordinate descent algorithm with the generalized thresholding operator. Our Consensus RegularIzation Selection based Prediction (CRISP) model is evaluated on electronic health records (EHRs) obtained from a large hospital for the congestive heart failure readmission prediction problem. We also evaluate our model on high-dimensional synthetic datasets to assess its performance. The results indicate that CRISP outperforms several state-of-the-art methods such as additive, interactions-based and other competing non-convex regularized linear regression methods.
Empowering Truth Discovery with Multi-Truth Prediction
Authors: Xianzhi Wang, Quan Z. Sheng, Lina Yao, Xue Li, Xiu Susie Fang, Xiaofei Xu, Boualem Benatallah
Venue: CIKM
Year: 2016

Truth discovery is the problem of detecting true values from the conflicting data provided by multiple sources on the same data items. Since sources' reliability is unknown a priori, a truth discovery method usually estimates sources' reliability along with the truth discovery process. A major limitation of existing truth discovery methods is that they commonly assume exactly one true value on each data item and therefore cannot deal with the more general case that a data item may have multiple true values (or multi-truth). Since the number of true values may vary from data item to data item, this requires truth discovery methods being able to detect varying numbers of truth values from the multi-source data. In this paper, we propose a multi-truth discovery approach, which addresses the above challenges by providing a generic framework for enhancing existing truth discovery methods. In particular, we redeem the numbers of true values as an important clue for facilitating multi-truth discovery. We present the procedure and components of our approach, and propose three models, namely the byproduct model, the joint model, and the synthesis model to implement our approach. We further propose two extensions to enhance our approach, by leveraging the implications of similar numerical values and values' co-occurrence information in sources' claims to improve the truth discovery accuracy. Experimental studies on real-world datasets demonstrate the effectiveness of our approach.
Paired Restricted Boltzmann Machine for Linked Data
Authors: Suhang Wang, Jiliang Tang, Fred Morstatter, Huan Liu
Venue: CIKM
Year: 2016

Restricted Boltzmann Machines (RBMs) are widely adopted unsupervised representation learning methods and have powered many data mining tasks such as collaborative filtering and document representation. Recently, linked data that contains both attribute and link information has become ubiquitous in various domains. For example, social media data is inherently linked via social relations and web data is networked via hyperlinks. It is evident from recent work that link information can enhance a number of real-world applications such as clustering and recommendations. Therefore, link information has the potential to advance RBMs for better representation learning. However, the majority of existing RBMs have been designed for independent and identically distributed data and are unequipped for linked data. In this paper, we aim to design a new type of Restricted Boltzmann Machines that takes advantage of linked data. In particular, we propose a paired Restricted Boltzmann Machine (pRBM), which is able to leverage the attribute and link information of linked data for representation learning. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework pRBM.
Supervised Feature Selection by Preserving Class Correlation
Authors: Jun Wang, Jinmao Wei, Zhenglu Yang
Venue: CIKM
Year: 2016

Feature selection is an effective technique for dimension reduction, which assesses the importance of features and constructs an optimal feature subspace suitable for recognition task. Two recognition scenarios, i.e., single-label learning and multi-label learning, pose different challenges for feature selection. For the single-label task, how to accurately measure and reduce feature redundancy is crucial. For the multi-label task, how to effectively exploit class correlation information during selection is critical. However, both issues cannot be simultaneously resolved by any existing selection methods. In this paper, we propose effective supervised feature selection techniques to address the problems. The original class correlation information in the reduced feature space is preserved, and meanwhile the feature redundancy for classification is alleviated. To the best of our knowledge, this study is the first attempt to accomplish both recognition tasks in a unified framework. Comprehensive experimental evaluations on artificial, single-label, and multi-label data sets demonstrate the effectiveness of the new approach.
Efficient Distributed Regular Path Queries on RDF Graphs Using Partial Evaluation
Authors: Xin Wang, Junhu Wang, Xiaowang Zhang
Venue: CIKM
Year: 2016

We propose an efficient distributed method for answering regular path queries (RPQs) on large-scale RDF graphs using partial evaluation. In local computation, we devise a dynamic programming approach to evaluate local and partial answers of an RPQ on each computing site in parallel. In the assembly phase, an automata-based algorithm is proposed to assemble the partial answers of the RPQ into the final results. The experiments on benchmark RDF graphs show that our method outperforms the state-of-the-art message passing methods by up to an order of magnitude.
Error Link Detection and Correction in Wikipedia
Authors: Chengyu Wang, Rong Zhang, Xiaofeng He, Aoying Zhou
Venue: CIKM
Year: 2016

The hyperlink structure of Wikipedia forms a rich semantic network connecting entities and concepts, enabling it as a valuable source for knowledge harvesting. Wikipedia, as crowd-sourced data, faces various data quality issues which significantly impacts knowledge systems depending on it as the information source. One such issue occurs when an anchor text in a Wikipage links to a wrong Wikipage, causing the error link problem. While much of previous work has focused on leveraging Wikipedia for entity linking, little has been done to detect error links.
A Density-Based Approach to the Retrieval of Top-K Spatial Textual Clusters
Authors: Dingming Wu, Christian S. Jensen
Venue: CIKM
Year: 2016

Spatial keyword queries retrieve spatial textual objects that are near a query location and are relevant to query keywords. The paper defines the top-k spatial textual clusters (k-STC) query that returns the top-k clusters that are located close to a given query location, contain relevant objects with regard to given query keywords, and have an object density that exceeds a given threshold. This query aims to support users who wish to explore nearby regions with many relevant objects. To compute this query, the paper proposes a basic and an advanced algorithm that rely on on-line density-based clustering. An empirical study offers insight into the performance properties of the proposed algorithms.
Where Did You Go: Personalized Annotation of Mobility Records
Authors: Fei Wu, Zhenhui Li
Venue: CIKM
Year: 2016

Recent advances in positioning technology have generated massive volume of human mobility data. At the same time, large amount of spatial context data are available and provide us with rich context information. Combining the mobility data with surrounding spatial context enables us to understand the semantics of the mobility records, e.g., what is a user doing at a location, e.g., dining at a restaurant or attending a football game). In this paper, we aim to answer this question by annotating the mobility records with surrounding venues that were actually visited by the user. The problem is non-trivial due to high ambiguity of surrounding contexts. Unlike existing methods that annotate each location record independently, we propose to use all historical mobility records to capture user preferences, which results in more accurate annotations. Our method does not assume the availability to any training data on user preference because of the difficulties to obtain such data in the real-world setting. Instead, we design a Markov random field model to find the best annotations that maximize the consistency of annotated venues. Through extensive experiments on real datasets, we demonstrate that our method significantly outperforms the baseline methods.
Active Zero-Shot Learning
Authors: Sihong Xie, Shaoxiong Wang, Philip S. Yu
Venue: CIKM
Year: 2016

In multi-label classification in the big data age, the number of classes can be in thousands, and obtaining sufficient training data for each class is infeasible. Zero-shot learning aims at predicting a large number of unseen classes using only labeled data from a small set of classes and external knowledge about class relations. However, previous zero-shot learning models passively accept labeled data collected beforehand, relinquishing the opportunity to select the proper set of classes to inquire labeled data and optimize the performance of unseen class prediction. To resolve this issue, we propose an active class selection strategy to intelligently query labeled data for a parsimonious set of informative classes. We demonstrate two desirable probabilistic properties of the proposed method that can facilitate unseen classes prediction. Experiments on 4 text datasets demonstrate that the active zero-shot learning algorithm is superior to a wide spectrum of baselines. We indicate promising future directions at the end of this paper.
Effective and Efficient Spectral Clustering on Text and Link Data
Authors: Zhiqiang Xu, Yiping Ke
Venue: CIKM
Year: 2016

Clustering text and link data, as an important task in text and link analysis, aims at finding communities of linked documents by leveraging the information from both domains. Due to its improved performance over the single domain counterpart, it has attracted increasing attention from practitioners in recent years. Despite its popularity, all existing algorithms on clustering text and link data overlook the existence of domain-specific distinctions and thus result in unsatisfactory clustering quality. In this paper, we address this limitation by explicitly modeling the domain-specific distinctions in the clustering process. Specifically, we extend the idea of consensus and domain-specific subspace decomposition from flat data to graph data. Such a modeling, when coupled with a regularization to further sharpen the information distinction, makes the consensus information between text and link more accurate for clustering with both domains. The final model is cast into the spectral clustering model by imposing the subspace orthogonality. To eschew the costly eigen-decomposition required for spectral clustering and further speed-up the optimization, we take advantage of the data sparsity and the low dimensionality of subspaces, and deploy a constraint-preserving gradient method to efficiently solve the model. The experimental study on three real datasets shows that our algorithm consistently and significantly outperforms the state-of-the-art relevant algorithms in terms of both quality and efficiency.