OpenRaiser/Intern-Atlas huggingface dataset:
Combining geographical information of users and content of items for accurate rating prediction
Authors: Zhi Qiao, Peng Zhang, Jing He, Yanan Cao, Chuan Zhou, Li Guo
Venue: Conference WWW 2014
Year: 2014

Recommender systems have attracted attentions lately due to their wide and successful applications in online advertising. In this paper, we propose a bayesian generative model to describe the generative process of rating, which combines geographical information of users and content of items. The generative model consists of two interacting LDA models, where one LDA model for location-based user groups (user dimension) and the other for the topics of content of items(item dimension). A Gibbs sampling algorithm is proposed for parameter estimation. Experiments have shown our proposed method outperforms baseline methods.
Pedestrian detection in infrared images based on local shape features
Authors: Li Zhang, Bo Wu 0001, Ram Nevatia
Venue: Conference CVPR 2007
Year: 2007

Use of IR images is advantageous for many surveillance applications where the systems must operate around the clock and external illumination is not always available. We investigate the methods derived from visible spectrum analysis for the task of human detection. Two feature classes (edgelets and HOG features) and two classification models(AdaBoost and SVM cascade) are extended to IR images. We find out that it is possible to get detection performance in IR images that is comparable to state-of-the-art results for visible spectrum images. It is also shown that the two domains share many features, likely originating from the silhouettes, in spite of the starkly different appearances of the two modalities.
Mashup-based information retrieval for domain experts
Authors: Anand Ranganathan, Anton Riabov, Octavian Udrea
Venue: Conference CIKM 2009
Year: 2009

In this paper, we tackle the problem of helping domain experts to construct, parameterize and deploy mashups of data and code. We view a mashup as a data processing flow, that describes how data is obtained from one or more sources, processed by one or more components, and finally sent to one or more sinks. Our approach allows specifying patterns of flows, in a language called Cascade. The patterns cover different possible variations of the flows, including variations in the structure of the flow, the components in the flow and the possible parameterizations of these components. We present a tool that makes use of this knowledge of flow patterns and associated metadata to allow domain experts to explore the space of possible flows described in the pattern. The tool uses an AI planning approach to automatically build a flow, belonging to the flow pattern, from a high-level goal, specified as a set of tags. We describe examples from the financial services domain to show the use of flow patterns in allowing domain experts to construct a large variety of mashups rapidly.
Decomposing background topics from keywords by principal component pursuit
Authors: Kerui Min, Zhengdong Zhang, John Wright, Yi Ma
Venue: Conference CIKM 2010
Year: 2010

Low-dimensional topic models have been proven very useful for modeling a large corpus of documents that share a relatively small number of topics. Dimensionality reduction tools such as Principal Component Analysis or Latent Semantic Indexing (LSI) have been widely adopted for document modeling, analysis, and retrieval. In this paper, we contend that a more pertinent model for a document corpus as the combination of an (approximately) low-dimensional topic model for the corpus and a sparse model for the keywords of individual documents. For such a joint topic-document model, LSI or PCA is no longer appropriate to analyze the corpus data. We hence introduce a powerful new tool called Principal Component Pursuit that can effectively decompose the low-dimensional and the sparse components of such corpus data. We give empirical results on data synthesized with a Latent Dirichlet Allocation (LDA) mode to validate the new model. We then show that for real document data analysis, the new tool significantly reduces the perplexity and improves retrieval performance compared to classical baselines.
Online multi-modal distance learning for scalable multimedia retrieval
Authors: Hao Xia, Pengcheng Wu, Steven C. H. Hoi
Venue: Conference WSDM 2013
Year: 2013

In many real-word scenarios, e.g., multimedia applications, data often originates from multiple heterogeneous sources or are represented by diverse types of representation, which is often referred to as "multi-modal data". The definition of distance between any two objects/items on multi-modal data is a key challenge encountered by many real-world applications, including multimedia retrieval. In this paper, we present a novel online learning framework for learning distance functions on multi-modal data through the combination of multiple kernels. In order to attack large-scale multimedia applications, we propose Online Multi-modal Distance Learning (OMDL) algorithms, which are significantly more efficient and scalable than the state-of-the-art techniques. We conducted an extensive set of experiments on multi-modal image retrieval applications, in which encouraging results validate the efficacy of the proposed technique.
Modeling dual role preferences for trust-aware recommendation
Authors: Weilong Yao, Jing He, Guangyan Huang, Yanchun Zhang
Venue: Conference SIGIR 2014
Year: 2014

Unlike in general recommendation scenarios where a user has only a single role, users in trust rating network, e.g. Epinions, are associated with two different roles simultaneously: as a truster and as a trustee. With different roles, users can show distinct preferences for rating items, which the previous approaches do not involve. Moreover, based on explicit single links between two users, existing methods can not capture the implicit correlation between two users who are similar but not socially connected. In this paper, we propose to learn dual role preferences (truster/trustee-specific preferences) for trust-aware recommendation by modeling explicit interactions (e.g., rating and trust) and implicit interactions. In particular, local links structure of trust network are exploited as two regularization terms to capture the implicit user correlation, in terms of truster/trustee-specific preferences. Using a real-world and open dataset, we conduct a comprehensive experimental study to investigate the performance of the proposed model, RoRec. The results show that RoRec outperforms other trust-aware recommendation approaches, in terms of prediction accuracy.
Characterizing commercial intent
Authors: Azin Ashkan, Charles L. A. Clarke
Venue: Conference CIKM 2009
Year: 2009

Understanding the intent underlying user's queries may help personalize search results and therefore improve user satisfaction. We develop a methodology for using the content of search engine result pages (SERPs) along with the information obtained from query strings to study characteristics of query intent, with a particular focus on sponsored search. This work represents an initial step towards the development and evaluation of an ontology for commercial search, considering queries that reference specific products, brands and retailers. The characteristics of query categories are studied with respect to aggregated user's clickthrough behavior on advertising links. We present a model for clickthrough behavior that considers the influence of such factors as the location of ads and the rank of ads, along with query category. We evaluate our work using a large corpus of clickthrough data obtained from a major commercial search engine. Our findings suggest that query based features, along with the content of SERPs, are effective in detecting query intent. The clickthrough behavior is found to be consistent with the classification for the general categories of query intent, while for product, brand and retailer categories, all is true to a lesser extent.
Term-based commercial intent analysis
Authors: Azin Ashkan, Charles L. A. Clarke
Venue: Conference SIGIR 2009
Year: 2009

In this work, we investigate the contribution of query terms and their corresponding ad click rates on commercial intent of queries. A probabilistic model is proposed following the hypothesis that a query is likely to receive ad clicks based on contributions from its individual terms.
Ready to buy or just browsing?: detecting web searcher goals from interaction data
Authors: Qi Guo, Eugene Agichtein
Venue: Conference SIGIR 2010
Year: 2010

An improved understanding of the relationship between search intent, result quality, and searcher behavior is crucial for improving the effectiveness of web search. While recent progress in user behavior mining has been largely focused on aggregate server-side click logs, we present a new class of search behavior models that also exploit fine-grained user interactions with the search results.
Predicting purchase behaviors from social media
Authors: Yongzheng Zhang, Marco Pennacchiotti
Venue: Conference WWW 2013
Year: 2013

In the era of social commerce, users often connect from e-commerce websites to social networking venues such as Facebook and Twitter. However, there have been few efforts on understanding the correlations between users' social media profiles and their e-commerce behaviors. This paper presents a system for predicting a user's purchase behaviors on e-commerce websites from the user's social media profile. We specifically aim at understanding if the user's profile information in a social network (for example Facebook) can be leveraged to predict what categories of products the user will buy from (for example eBay Electronics). The paper provides an extensive analysis on how users' Facebook profile information correlates to purchases on eBay, and analyzes the performance of different feature sets and learning algorithms on the task of purchase behavior prediction.
Mining novelty-seeking trait across heterogeneous domains
Authors: Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie
Venue: Conference WWW 2014
Year: 2014

An incisive understanding of personal psychological traits is not only essential to many scientific disciplines, but also has a profound business impact on online recommendation. Recent studies in psychology suggest that novelty-seeking trait is highly related to consumer behavior. In this paper, we focus on understanding individual novelty-seeking trait embodied at different levels and across heterogeneous domains. Unlike the questionnaire-based methods widely adopted in the past, we first present a computational framework, Novel Seeking Model (NSM), for exploring the novelty-seeking trait implied by observable activities. Then, we explore the novelty-seeking trait in two heterogeneous domains: check-in behavior in location based social networks, which reflects mobility patterns in the physical world, and online shopping behavior on e-commerce sites, which reflects consumption concepts in economic activities. To demonstrate the effectiveness of NSM, we conducted extensive experiments, with a large dataset covering the two-domain activities for hundreds of thousands of individuals. Our results suggest that NSM offers a powerful paradigm for 1) presenting an effective measurement of a personality trait that can explicitly explain the deviation of individuals from the habits of individuals and crowds; 2) uncovering the correlation of novelty-seeking trait at different levels and across heterogeneous domains. The proposed method provides emerging implications for personalized cross-domain recommendation and targeted advertising.
Recommending citations with translation model
Authors: Yang Lu, Jing He, Dongdong Shan, Hongfei Yan
Venue: Conference CIKM 2011
Year: 2011

Citation Recommendation is useful for an author to find out the papers or books that can support the materials she is writing about. It is a challengeable problem since the vocabulary used in the content of papers and in the citation contexts are usually quite different. To address this problem, we propose to use translation model, which can bridge the gap between two heterogeneous languages. We conduct an experiment and find the translation model can provide much better candidates of citations than the state-of-the-art methods.
Social action tracking via noise tolerant time-varying factor graphs
Authors: Chenhao Tan, Jie Tang, Jimeng Sun, Quan Lin, Fengjiao Wang
Venue: Conference KDD 2010
Year: 2010

It is well known that users' behaviors (actions) in a social network are influenced by various factors such as personal interests, social influence, and global trends. However, few publications systematically study how social actions evolve in a dynamic social network and to what extent different factors affect the user actions. In this paper, we propose a Noise Tolerant Time-varying Factor Graph Model (NTT-FGM) for modeling and predicting social actions. NTT-FGM simultaneously models social network structure, user attributes and user action history for better prediction of the users' future actions. More specifically, a user's action at time t is generated by her latent state at t, which is influenced by her attributes, her own latent state at time t-1 and her neighbors' states at time t and t-1. Based on this intuition, we formalize the social action tracking problem using the NTT-FGM model; then present an efficient algorithm to learn the model, by combining the ideas from both continuous linear system and Markov random field. Finally, we present a case study of our model on predicting future social actions. We validate the model on three different types of real-world data sets. Qualitatively, our model can uncover some interesting patterns of the social dynamics. Quantitatively, experimental results show that the proposed method outperforms several baseline methods for action prediction.
Discovering leaders from community actions
Authors: Amit Goyal, Francesco Bonchi, Laks V. S. Lakshmanan
Venue: Conference CIKM 2008
Year: 2008

We introduce a novel frequent pattern mining approach to discover leaders and tribes in social networks. In particular, we consider social networks where users perform actions. Actions may be as simple as tagging resources (urls) as in del.icio.us, rating songs as in Yahoo! Music, or movies as in Yahoo! Movies, or users buying gadgets such as cameras, handhelds, etc. and blogging a review on the gadgets. The assumption is that actions performed by a user can be seen by their network friends. Users seeing their friends' actions are sometimes tempted to perform those actions. We are interested in the problem of studying the propagation of such "influence", and on this basis, identifying which users are leaders when it comes to setting the trend for performing various actions. We consider alternative definitions of leaders based on frequent patterns and develop algorithms for their efficient discovery. Our definitions are based on observing the way influence propagates in a time window, as the window is moved in time. Given a social graph and a table of user actions, our algorithms can discover leaders of various flavors by making one pass over the actions table. We run detailed experiments to evaluate the utility and scalability of our algorithms on real-life data. The results of our experiments confirm on the one hand, the efficiency of the proposed algorithm, and on the other hand, the effectiveness and relevance of the overall framework. To the best of our knowledge, this the first frequent pattern based approach to social network mining.
Yes, there is a correlation: - from social networks to personal behavior on the web
Authors: Parag Singla, Matthew Richardson
Venue: Conference WWW 2008
Year: 2008

Characterizing the relationship that exists between a person's social group and his/her personal behavior has been a long standing goal of social network analysts. In this paper, we apply data mining techniques to study this relationship for a population of over 10 million people, by turning to online sources of data. The analysis reveals that people who chat with each other (using instant messaging) are more likely to share interests (their Web searches are the same or topically similar). The more time they spend talking, the stronger this relationship is. People who chat with each other are also more likely to share other personal characteristics, such as their age and location (and, they are likely to be of opposite gender). Similar findings hold for people who do not necessarily talk to each other but do have a friend in common. Our analysis is based on a well-defined mathematical formulation of the problem, and is the largest such study we are aware of.
Trust prediction via aggregating heterogeneous social networks
Authors: Jin Huang, Feiping Nie, Heng Huang, Yi-Cheng Tu
Venue: Conference CIKM 2012
Year: 2012

Along with the increasing popularity of social web sites, users rely more on the trustworthiness information for many online activities among users. However, such social network data often suffers from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches explore the topology of trust graph. Previous research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behavior and tastes. Such ancillary information, is often accessible and therefore could potentially help the trust prediction. In this paper, we address the link prediction problem by aggregating heterogeneous social networks and propose a novel joint manifold factorization (JMF) method. Our new joint learning model explores the user group level similarity between correlated graphs and simultaneously learns the individual graph structure, therefore the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph, but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the objective function, we break down the proposed objective function into several manageable sub-problems, then further establish the theoretical convergence with the aid of auxiliary function. Extensive experiments were conducted on real world data sets and all empirical results demonstrated the effectiveness of our method.
How status and reputation shape human evaluations: consequences for recommender systems
Authors: Jure Leskovec
Venue: Conference WWW 2013
Year: 2013

Recommender systems are inherently driven by evaluations and reviews provided by the users of these systems. Understanding ways in which users form judgments and produce evaluations can provide insights for modern recommendation systems. Many online social applications include mechanisms for users to express evaluations of one another, or of the content they create. In a variety of domains, mechanisms for evaluation allow one user to say whether he or she trusts another user, or likes the content they produced, or wants to confer special levels of authority or responsibility on them. We investigate a number of fundamental ways in which user and item characteristics affect the evaluations in online settings. For example, evaluations are not unidimensional but include multiple aspects that all together contribute to user's overall rating. We investigate methods for modeling attitudes and attributes from online reviews that help us better understand user's individual preferences. We also examine how to create a composite description of evaluations that accurately reflects some type of cumulative opinion of a community. Natural applications of these investigations include predicting the evaluation outcomes based on user characteristics and to estimate the chance of a favorable overall evaluation from a group knowing only the attributes of the group's members, but not their expressed opinions.
Trust in social computing
Authors: Jiliang Tang, Huan Liu
Venue: Conference WWW 2014
Year: 2014

The rapid development of social media exacerbates the information overload and credibility problems. Trust, providing information about with whom we can trust to share information and from whom we can accept information, plays an important role in helping users collect relevant and reliable information in social media. Trust has become a research topic of increasing importance and of practical significance. In this tutorial, we illustrate properties and representation models of trust, elucidate trust measurements with representative algorithms, and demonstrate real-world applications where trust is explicitly used. As a new dimension of the trust study, we discuss the concept of distrust and its roles in trust measurements and applications.
Mining user interest from search tasks and annotations
Authors: Sampath Jayarathna, Atish Patra, Frank Shipman
Venue: Conference CIKM 2013
Year: 2013

Interactive web search involves selecting which documents to read further and locating the parts of the documents that are relevant to the user's current activity. In this paper, we introduce UIMaP: User Interest Modeling and Personalization, a search task based personal user interest model to support users' information gathering tasks. The novelty of our approach lies in the use of topic modeling to generate fine-grained models of user interest and visualizations that direct user's attention to documents or parts of documents that match user's inferred interests. User annotations are used to help generate personalized visualizations for user's search tasks. Based on 1267 user annotations from 17 users, we show the performance comparisons of four different topic models: LDA+H, LDA+KL, LDA+JSD, and LDA+TopN.
On the equivalent of low-rank linear regressions and linear discriminant analysis based regressions
Authors: Xiao Cai, Chris H. Q. Ding, Feiping Nie, Heng Huang
Venue: Conference KDD 2013
Year: 2013

The low-rank regression model has been studied and applied to capture the underlying classes/tasks correlation patterns, such that the regression/classification results can be enhanced. In this paper, we will prove that the low-rank regression model is equivalent to doing linear regression in the linear discriminant analysis (LDA) subspace. Our new theory reveals the learning mechanism of low-rank regression, and shows that the low-rank structures exacted from classes/tasks are connected to the LDA projection results. Thus, the low-rank regression efficiently works for the high-dimensional data.
Optimal exact least squares rank minimization
Authors: Shuo Xiang, Yunzhang Zhu, Xiaotong Shen, Jieping Ye
Venue: Conference KDD 2012
Year: 2012

In multivariate analysis, rank minimization emerges when a low-rank structure of matrices is desired as well as a small estimation error. Rank minimization is nonconvex and generally NP-hard, imposing one major challenge. In this paper, we consider a nonconvex least squares formulation, which seeks to minimize the least squares loss function with the rank constraint. Computationally, we develop efficient algorithms to compute a global solution as well as an entire regularization solution path. Theoretically, we show that our method reconstructs the oracle estimator exactly from noisy data. As a result, it recovers the true rank optimally against any method and leads to sharper parameter estimation over its counterpart. Finally, the utility of the proposed method is demonstrated by simulations and image reconstruction from noisy background.