OpenRaiser/Intern-Atlas huggingface dataset:
Location-sensitive resources recommendation in social tagging systems
Authors: Chang Wan, Ben Kao, David W. Cheung
Venue: CIKM
Year: 2012

In social tagging systems, resources such as images and videos are annotated with descriptive words called tags. It has been shown that tag-based resource searching and retrieval is much more effective than content-based retrieval. With the advances in mobile technology, many resources are also geo-tagged with location information. We observe that a traditional tag (word) can carry different semantics at different locations. We study how location information can be used to help distinguish the different semantics of a resource's tags and thus to improve retrieval accuracy. Given a search query, we propose a location-partitioning method that partitions all locations into regions such that the user query carries distinguishing semantics in each region. Based on the identified regions, we utilize location information in estimating the ranking scores of resources for the given query. These ranking scores are learned using the Bayesian Personalized Ranking (BPR) framework. Two algorithms, namely, LTD and LPITF, which apply Tucker Decomposition and Pairwise Interaction Tensor Factorization, respectively for modeling the ranking score tensor are proposed. Through experiments on real datasets, we show that LTD and LPITF outperform other tag-based resource retrieval methods.
Learning to predict the cost-per-click for your ad words
Authors: Chieh-Jen Wang, Hsin-Hsi Chen
Venue: CIKM
Year: 2012

In Internet ad campaign, ranking of an ad on search result pages depends on a cost-per-click (CPC) of ad words offered by an advertiser and a quality score estimated by a search engine. Bidding for ad words with a higher CPC is more competitive than bidding for the same ad words with a lower CPC in the ad ranking competition. However, offering a higher CPC will increase a burden on advertisers. In contrast, offering a lower CPC may decrease the exposure rate of their ads. Thus, how to select an appropriate CPC for ad words is indispensable for advertisers. In this paper, we extract different semantic levels of features, such as named entities, topic terminologies, and individual words from a large-scale real-world ad words corpus, and explore various learning based prediction algorithms. The thorough experimental results show that the CPC prediction models considering more ad words semantics achieve better prediction performance, and the prediction model using the support vector regression (SVR) and features from all semantic levels performs the best.
Polygene-based evolution: a novel framework for evolutionary algorithms
Authors: Shuaiqiang Wang, Byron J. Gao, Shuangling Wang, Guibao Cao, Yilong Yin
Venue: CIKM
Year: 2012

In this paper, we introduce polygene-based evolution, a novel framework for evolutionary algorithms (EAs) that features distinctive operations in the evolution process. In traditional EAs, the primitive evolution unit is gene, where genes are independent components during evolution. In polygene-based evolutionary algorithms (PGEAs), the evolution unit is polygene, i.e., a set of co-regulated genes. Discovering and maintaining quality polygenes can play an effective role in evolving quality individuals. Polygenes generalize genes, and PGEAs generalize EAs. Implementing the PGEA framework involves three phases: polygene discovery, polygene planting, and polygene-compatible evolution. Extensive experiments on function optimization benchmarks in comparison with the conventional and state-of-the-art EAs demonstrate the potential of the approach in accuracy and efficiency improvement.
A unified learning framework for auto face annotation by mining web facial images
Authors: Dayong Wang, Steven Chu-Hong Hoi, Ying He
Venue: CIKM
Year: 2012

Auto face annotation plays an important role in many real-world multimedia information and knowledge management systems. Recently there is a surge of research interests in mining weakly-labeled facial images on the internet to tackle this long-standing research challenge in computer vision and image understanding. In this paper, we present a novel unified learning framework for face annotation by mining weakly labeled web facial images through interdisciplinary efforts of combining sparse feature representation, content-based image retrieval, transductive learning and inductive learning techniques. In particular, we first introduce a new search-based face annotation paradigm using transductive learning, and then propose an effective inductive learning scheme for training classification-based annotators from weakly labeled facial images, and finally unify both transductive and inductive learning approaches to maximize the learning efficacy. We conduct extensive experiments on a real-world web facial image database, in which encouraging results show that the proposed unified learning scheme outperforms the state-of-the-art approaches.
Author-conference topic-connection model for academic network search
Authors: Jianwen Wang, Xiaohua Hu, Xinhui Tu, Tingting He
Venue: CIKM
Year: 2012

This paper proposes a novel topic model, Author-Conference Topic-Connection (ACTC) Model for academic network search. The ACTC Model extends the author-conference-topic (ACT) model by adding subject of the conference and the latent mapping information between subjects and topics. It simultaneously models topical aspects of papers, authors and conferences with two latent topic layers: a subject layer corresponding to conference topic, and a topic layer corresponding to the word topic. Each author would be associated with a multinomial distribution over subjects of conference (eg., KM, DB, IR for CIKM 2012), the conference(CIKM 2012), and the topics are respectively generated from a sampled subject. Then the words are generated from the sampled topics. We conduct experiments on a data set with 8,523 authors, 22,487 papers and 1,243 conferences from the well-known Arnetminer website, and train the model with different number of subjects and topics. For a qualitative evaluation, we compare ACTC with three others models LDA, Author-Topic (AT) and ACT in academic search services. Experiments show that ACTC can effectively capture the semantic connection between different types of information in academic network and perform well in expert searching and conference searching.
Influence and similarity on heterogeneous networks
Authors: Guan Wang, Qingbo Hu, Philip S. Yu
Venue: CIKM
Year: 2012

In the social network research, the studies on social influence maximization and entity similarity are two important and orthogonal tasks. On homogeneous networks, social influence maximization research tries to identify an initial influential set that maximizes the spread of the information, while similarity studies focus on designing meaningful ways to quantify entities' similarities. When heterogeneous networks are becoming ubiquitous and entities of different types are related to each other, we observe the possibility of merging the two directions together to improve the performance for both of them. In fact, we found that influence values among one type of nodes and similarity scores among the other type of nodes reinforce each other towards better and more meaningful results. Therefore, we introduce a framework that computes social influence for one type of nodes and simultaneously measures similarity of the other type of nodes in a heterogeneous network. First, we decouple the target heterogeneous network (or we call it Influence Similarity (IS) network) into three different parts: Influence network, Similarity network and information tunnels (IT) between them. Through IT, we exchange the influence scores and the similarity scores to calculate more precise similarity and influence scores in order to improve both of their qualities. The experiment results on real world data shows that our framework enables influence maximization framework to identify more influential seeds in Influence network and similarity measures to produce more meaningful similarity scores in Similarity network simultaneously.
Improving bag-of-visual-words model with spatial-temporal correlation for video retrieval
Authors: Lei Wang, Dawei Song, Eyad Elyan
Venue: CIKM
Year: 2012

Most of the state-of-art approaches to Query-by-Example (QBE) video retrieval are based on the Bag-of-visual-Words (BovW) representation of visual content. It, however, ignores the spatial-temporal information, which is important for similarity measurement between videos. Direct incorporation of such information into the video data representation for a large scale data set is computationally expensive in terms of storage and similarity measurement. It is also static regardless of the change of discriminative power of visual words for different queries. To tackle these limitations, in this paper, we propose to discover Spatial-Temporal Correlations (STC) imposed by the query example to improve the BovW model for video retrieval. The STC, in terms of spatial proximity and relative motion coherence between different visual words, is crucial to identify the discriminative power of the visual words. We develop a novel technique to emphasize the most discriminative visual words for similarity measurement, and incorporate this STC-based approach into the standard inverted index architecture. Our approach is evaluated on the TRECVID2002 and CC\_WEB\_VIDEO datasets for two typical QBE video retrieval tasks respectively. The experimental results demonstrate that it substantially improves the BovW model as well as a state of the art method that also utilizes spatial-temporal information for QBE video retrieval.
Adapting vector space model to ranking-based collaborative filtering
Authors: Shuaiqiang Wang, Jiankai Sun, Byron J. Gao, Jun Ma
Venue: CIKM
Year: 2012

Collaborative filtering (CF) is an effective technique addressing the information overload problem. Recently ranking-based CF methods have shown advantages in recommendation accuracy, being able to capture the preference similarity between users even if their rating scores differ significantly. In this study, we seek accuracy improvement of ranking-based CF through adaptation of the vector space model, where we consider each user as a document and her pairwise relative preferences as terms. We then use a novel degree-specialty weighting scheme resembling TF-IDF to weight the terms. Then we use cosine similarity to select a neighborhood of users for the target user to make recommendations. Experiments on benchmarks in comparison with the state-of-the-art methods demonstrate the promise of our approach.
A novel local patch framework for fixing supervised learning models
Authors: Yilei Wang, Bingzheng Wei, Jun Yan, Yang Hu, Zhi-Hong Deng, Zheng Chen
Venue: CIKM
Year: 2012

In the past decades, machine learning models, especially supervised learning algorithms, have been widely used in various real world applications. However, no matter how strong a learning model is, it will suffer from the prediction errors when it is applied to real world problems. Due to the black box nature of supervised learning models, it is a challenging problem to fix the supervised learning models by further learning from the failure cases it generates. In this paper, we propose a novel Local Patch Framework (LPF) to locally fix supervised learning models by learning from its predicted failure cases. Since the learning models are generally globally optimized during training process, our proposed LPF assumes that most of the learning errors are led by local errors in the model. Thus we aim to break the black boxes of learning models by identifying and fixing the local errors of various models automatically. The proposed LPF has two key steps, which are local error region subspace learning and local patch model learning. Through this way, we aim to fix the errors of learning models locally and automatically with certain generalization ability on unseen testing data. Experiments on both classification and ranking problems show that the proposed LPF is effective and outperforms the original algorithms and the incremental learning model.
Importance weighted passive learning
Authors: Shuaiqiang Wang, Xiaoming Xi, Yilong Yin
Venue: CIKM
Year: 2012

Importance weighted active learning (IWAL) introduces a weighting scheme to measure the importance of each instance for correcting the sampling bias of the probability distributions between training and test datasets. However, the weighting scheme of IWAL involves the distribution of the test data, which can be straightforwardly estimated in active learning by interactively querying users for labels of selected test instances, but difficult for conventional learning where there are no interactions with users, referred as passive learning. In this paper, we investigate the insufficient sampling bias problem, i.e., bias occurs only because of insufficient samples, but the sampling process is unbiased. In doing this, we present two assumptions on the sampling bias, based on which we propose a practical weighting scheme for the empirical loss function in conventional passive learning, and present IWPL, an importance weighted passive learning framework. Furthermore, we provide IWSVM, an importance weighted SVM for validation. Extensive experiments demonstrate significant advantages of IWSVM on benchmarks and synthetic datasets.
From face-to-face gathering to social structure
Authors: Chunyan Wang, Mao Ye, Wang-Chien Lee
Venue: CIKM
Year: 2012

The rapid development of on-line social networking sites has dramatically changed the way people live and communicate. One particularly interesting phenomena came along with this development is the prominent role of various on-line networking portals played in scheduling and organizing off-line group events and activities. In this paper, we focus on studying the face-to-face(f2f) group formed through, or facilitated by, on-line portals. We first show the distinct characteristics of such f2f groups by analyzing datasets collected from Whrrl and Meetup. Next, we propose a dynamic model for group gathering based on the process of friend invitation to interpret how a f2f group is formed on-line. The results of our model are confirmed by empirical observations. Finally, we demonstrate that using such group information can effectively improve the accuracies of social tie inference and friend recommendation.
Diversionary comments under political blog posts
Authors: Jing Wang, Clement T. Yu, Philip S. Yu, Bing Liu, Weiyi Meng
Venue: CIKM
Year: 2012

An important issue that has been neglected so far is the identification of diversionary comments. Diversionary comments under political blog posts are defined as comments that deliberately twist the bloggers' intention and divert the topic to another one. The purpose is to distract readers from the original topic and draw attention to a new topic. Given that political blogs have significant impact on the society, we believe it is imperative to identify such comments. We then categorize diversionary comments into 5 types, and propose an effective technique to rank comments in descending order of being diversionary. To the best of our knowledge, the problem of detecting diversionary comments has not been studied so far. Our evaluation on 2,109 comments under 20 different blog posts from Digg.com shows that the proposed method achieves the high mean average precision (MAP) of 92.6%. Sensitivity analysis indicates that the effectiveness of the method is stable under different parameter settings.
Please spread: recommending tweets for retweeting with implicit feedback
Authors: Sheng Wang, Xiaobo Zhou, Ziqi Wang, Ming Zhang
Venue: CIKM
Year: 2012

Retweeting is the key mechanism of information diffusion on microblogging community. It is very challenging for user to choose the suitable tweets for retweeting, given the diverse and massive messages received and limited time on site. Therefore, it is crucial to design a recommender system that automatically recommends tweets for user to retweet. Recommending tweets for retweeting is different from conventional recommender system due to limited explicit feedback, high proportion of cold-start tweets and short tweet active time. In this paper, we propose a novel retweet recommendation (RTR) framework which leverages the implicit feedback to help user find the potential tweets he may want to retweet. RTR is divided into offline learning and online recommendation so that tweets can be taken into account as soon as it is published. In offline learning, we adapt a matrix factorization method based on BPR-OPT framework with implicit feedback to compensate the limited explicit feedback. RTR is able to recommend cold-start tweet based on its content. Extensive experiments on real-world microblogging community clearly show that RTR outperforms upon existing methods.
Alternative assessor disagreement and retrieval depth
Authors: William Webber, Praveen Chandar, Ben Carterette
Venue: CIKM
Year: 2012

Assessors are well known to disagree frequently on the relevance of documents to a topic, but the factors leading to assessor disagreement are still poorly understood. In this paper, we examine the relationship between the rank at which a document is returned by a set of retrieval systems and the likelihood that a second assessor will disagree with the relevance assessment of the initial assessor, and find that there is a strong and consistent correlation between the two. We adopt a metarank method of summarizing a document’s rank across multiple runs, and propose a logistic regression predictive model of second assessor disagreement given metarank and initially-assessed relevance. The consistency of the model parameters across different topics, assessor pairs, and collections is considered. The model gives comparatively accurate predictions of absolute system scores, but less consistent predictions of relative scores than a simpler rank-insensitive model. We demonstrate that the logistic regression model is robust to using sampled, rather than exhaustive, dual assessment. We demonstrate the use of the sampled predictive model to incorporate assessor disagreement into tests of statistical significance.
Advice for young Jedi knights and PhD students
Authors: Ingmar Weber
Venue: CIKM
Year: 2012

This talk will look at the task of pursuing a PhD with both a humorous and a serious eye -- while checking Facebook with yet another one. The goal is threefold: to give practical advice, to take a step back and look at the meaning at it all, and to entertain in doing so. We will give tips on how to choose an advisor and a thesis topic. We will look at different successful approaches to doing research and to publishing. In all of this the "human angle" is not lost and we will analyze common psychological hurdles. The practical advice will be based on a mix of personal experience, books on the topic and a multitude of web resources. Zooming out, we will look at whether education can contribute to personal happiness, and at financial incentives from the job market. Also "from a distance", we will look at studies of PhD attrition rates to better understand potential obstacles. Finally, there will be plenty of infotainment including the scientifically proven benefits of chocolate, per-occupation divorce rates and the affect of too much time spent on Facebook on your GPA. And, for the case that none of the advice is helpful for you, we will close with productive ways to procrastinate.
PLEAD 2012: politics, elections and data
Authors: Ingmar Weber, Ana-Maria Popescu, Marco Pennacchiotti
Venue: CIKM
Year: 2012

What is the role of the internet in politics general and during campaigns in particular? And what is the role of large amounts of user data in all of this? In the 2008 U.S. presidential campaign the Democrats were far more successful than the Republicans in utilizing online media for mobilization, co-ordination and fundraising. For the first time, social media and the Internet played a fundamental role in political campaigns. However, technical research in this area has been surprisingly limited and fragmented. The goal of this workshop is to bring together, for the first time, researchers working at the intersection of social network analysis, computational social science and political science, to share and discuss their ideas in a common forum; and to inspire further developments in this growing, fascinating field. The workshop has Filippo Menczer as keynote speaker, it includes technical presentations of accepted papers and concludes with a panel discussion where scientists and media experts from different fields can interact and share views.
Model the complex dependence structures of financial variables by using canonical vine
Authors: Wei Wei, Xuhui Fan, Jinyan Li, Longbing Cao
Venue: CIKM
Year: 2012

Financial variables such as asset returns in the massive market contain various hierarchical and horizontal relationships forming complicated dependence structures. Modeling and mining of these structures is challenging due to their own high structural complexities as well as the stylized facts of the market data. This paper introduces a new canonical vine dependence model to identify the asymmetric and non-linear dependence structures of asset returns without any prior independence assumptions. To simplify the model while maintaining its merit, a partial correlation based method is proposed to optimize the canonical vine. Compared with the original canonical vine, the new model can still maintain the most important dependence but many unimportant nodes are removed to simplify the canonical vine structure. Our model is applied to construct and analyze dependence structures of European stocks as case studies. Its performance is evaluated by measuring portfolio of Value at Risk, a widely used risk management measure. In comparison to a very recent canonical vine model and the 'full' model, our experimental results demonstrate that our model has a much better quality of Value at Risk, providing insightful knowledge for investors to control and reduce the aggregation risk of the portfolio.
Fast and accurate incremental entity resolution relative to an entity knowledge base
Authors: Michael J. Welch, Aamod Sane, Chris Drome
Venue: CIKM
Year: 2012

User facing topical web applications such as events or shopping sites rely on large collections of data records about real world entities that are updated at varying latencies ranging from days to seconds. For example, event venue details are changed relatively infrequently whereas ticket pricing and availability for an event is often updated in near-realtime. Users regard these sites as high quality if they seldom show duplicates, the URLs are stable, and their content is fresh, so it is important to resolve duplicate entity records with high quality and low latencies. High quality entity resolution typically evaluates the entire record corpus for similar record clusters at the cost of latency, while low latency resolution examines the least possible entities to keep time to a minimum, even at the cost of quality. In this paper we show how to keep low latency while achieving high quality, combining the best of both approaches: given an entity to be resolved, our incremental Fastpath system, in a matter of milliseconds, makes approximately the same decisions that the underlying batch system would have made. Our experiments show that the Fastpath system makes matching decisions for previously unseen entities with 90% precision and 98% recall relative to batch decisions, with latencies under 20ms on commodity hardware.