OpenRaiser/Intern-Atlas huggingface dataset:
A graph-matching kernel for object categorization
Authors: Olivier Duchenne, Armand Joulin, Jean Ponce
Venue: Conference ICCV 2011
Year: 2011

This paper addresses the problem of category-level image classification. The underlying image model is a graph whose nodes correspond to a dense set of regions, and edges reflect the underlying grid structure of the image and act as springs to guarantee the geometric consistency of nearby regions during matching. A fast approximate algorithm for matching the graphs associated with two images is presented. This algorithm is used to construct a kernel appropriate for SVM-based image classification, and experiments with the Caltech 101, Caltech 256, and Scenes datasets demonstrate performance that matches or exceeds the state of the art for methods using a single type of features.
Inferring searcher attention by jointly modeling user interactions and content salience
Authors: Dmitry Lagun, Eugene Agichtein
Venue: Conference SIGIR 2015
Year: 2015

Modeling and predicting user attention is crucial for interpreting search behavior. The numerous applications include quantifying web search satisfaction, estimating search quality, and measuring and predicting online user engagement. While prior research has demonstrated the value of mouse cursor data and other interactions as a rough proxy of user attention, precisely predicting where a user is looking on a page remains a challenge, exacerbated in Web pages beyond the traditional search results. To improve attention prediction on a wider variety of Web pages, we propose a new way of modeling searcher behavior data by connecting the user interactions to the underlying Web page content. Specifically, we propose a principled model for predicting a searcher's gaze position on a page, that we call Mixture of Interactions and Content Salience (MICS). To our knowledge, our model is the first to effectively combine user interaction data, such as mouse cursor and scrolling positions, with the visual prominence, or salience, of the page content elements. Extensive experiments on multiple popular types of Web content demonstrate that the proposed MICS model significantly outperforms previous approaches to searcher gaze prediction that use only the interaction information. Grounding the observed interactions to the underlying page content provides a general and robust approach to user attention modeling, enabling more powerful tool for search behavior interpretation and ultimately search quality improvements.
Calibration-free gaze sensing using saliency maps
Authors: Yusuke Sugano, Yasuyuki Matsushita, Yoichi Sato 0001
Venue: Conference CVPR 2010
Year: 2010

We propose a calibration-free gaze sensing method using visual saliency maps. Our goal is to construct a gaze estimator only using eye images captured from a person watching a video clip. The key is treating saliency maps of the video frames as probability distributions of gaze points. To efficiently identify gaze points from saliency maps, we aggregate saliency maps based on the similarity of eye appearances. We establish mapping between eye images to gaze points by Gaussian process regression. The experimental result shows that the proposed method works well with different people and video clips and achieves 6 degrees of accuracy, which is useful for estimating a person's attention on monitors.
Partner tiering in display advertising
Authors: Anand Bhalgat, Nitish Korula, Hennadiy Leontyev, Max Lin, Vahab S. Mirrokni
Venue: Conference WSDM 2014
Year: 2014

Display ads on the Internet are often sold by publishers to advertisers in bundles of thousands or millions of impressions over a particular time period. The ad delivery systems assign ads to pages on behalf of publishers to satisfy these contracts, and at the same time, try to maximize the overall quality of assignment. This is usually modeled in the literature as an online allocation problem, where contracts are represented by overall delivery constraints.
Isotonic cca for sequence alignment and activity recognition
Authors: Shahriar Shariat, Vladimir Pavlovic 0001
Venue: Conference ICCV 2011
Year: 2011

This paper presents an approach for sequence alignment based on canonical correlation analysis(CCA). We show that a novel set of constraints imposed on traditional CCA leads to canonical solutions with the time warping property, i.e., non-decreasing monotonicity in time. This formulation generalizes the more traditional dynamic time warping (DTW) solutions to cases where the alignment is accomplished on arbitrary subsequence segments, optimally determined from data, instead on individual sequence samples. We then introduce a robust and efficient algorithm to find such alignments using non-negative least squares reductions. Experimental results show that this new method, when applied to MOCAP activity recognition problems, can yield improved recognition accuracy.
Enhancing investment decisions in p2p lending: an investor composition perspective
Authors: Chunyu Luo, Hui Xiong, Wenjun Zhou, Yanhong Guo, Guishi Deng
Venue: Conference KDD 2011
Year: 2011

P2P lending, as a novel economic lending model, has imposed new challenges about how to make effective investment decisions. Indeed, a key challenge along this line is how to align the right information with the right people. For a long time, people have made tremendous efforts in establishing credit records for the borrowers. However, information from investors is still under-explored for improving investment decisions in P2P lending. To that end, we propose a data driven investment decision-making framework, which exploits the investor composition of each investment for enhancing decisions making in P2P lending. Specifically, we first build investor profiles based on quantitative analysis of past performances, risk preferences, and investment experiences of investors. Then, based on investor profiles, we develop an investor composition analysis model, which can be used to select valuable investments and improve the investment decisions. To validate the proposed model, we perform extensive experiments on the real-world data from the world's largest P2P lending marketplace. Experimental results reveal that investor composition can help us evaluate the profit potential of an investment and the decision model based on investor composition can help investors make better investment decisions.
Portfolio selections in p2p lending: A multi-objective perspective
Authors: Hongke Zhao, Qi Liu, Guifeng Wang, Yong Ge, Enhong Chen
Venue: Conference KDD 2016
Year: 2016

P2P lending is an emerging wealth-management service for individuals, which allows lenders to directly bid and invest on the loans created by borrowers. In these platforms, lenders often pursue multiple objectives (e.g., non-default probability, fully-funded probability and winning-bid probability) when they select loans to invest. How to automatically assess loans from these objectives and help lenders select loan portfolios is a very important but challenging problem. To that end, in this paper, we present a holistic study on portfolio selections in P2P lending. Specifically, we first propose to adapt gradient boosting decision tree, which combines both static features and dynamic features, to assess loans from multiple objectives. Then, we propose two strategies, i.e., weighted objective optimization strategy and multi-objective optimization strategy, to select portfolios for lenders. For each lender, the first strategy attempts to provide one optimal portfolio while the second strategy attempts to provide a Pareto-optimal portfolio set. Further, we design two algorithms, namely DPA and EVA, which can efficiently resolve the optimizations in these two strategies, respectively. Finally, extensive experiments on a large-scale real-world data set demonstrate the effectiveness of our solutions.
Collaborative prediction for multi-entity interaction with hierarchical representation
Authors: Qiang Liu, Shu Wu, Liang Wang
Venue: Conference CIKM 2015
Year: 2015

With the rapid growth of Internet applications, there are more and more entities in interaction scenarios, and thus collaborative prediction for multi-entity interaction is becoming a significant problem. The state-of-the-art methods, e.g., tensor factorization and factorization machine, predict multi-entity interaction based on calculating the similarity among all entities. However, these methods are usually not able to reveal the joint characteristics of entities in the interaction. Besides, some methods may succeed in one specific application, but they can not be extended effectively for other applications or interaction scenarios with more entities. In this work, we propose a Hierarchical Interaction Representation (HIR) model, which models the mutual action among different entities as a joint representation. We generate the interaction representation of two entities via tensor multiplication, which is preformed iteratively to construct a hierarchical structure among all entities. Moreover, we employ several hidden layers to reveal the underlying properties of this interaction and enhance the model performance. After generating final representation, the prediction can be calculated using a variety of machine learning methods according to different tasks (i.e., linear regression for regression tasks, pair-wise ranking for ranking tasks and logistic regression for classification tasks). Experimental results show that our proposed HIR model yields significant improvements over the competitive compared methods in four different application scenarios (i.e., general recommendation, context-aware recommendation, latent collaborative retrieval and click-through rate prediction).
Modeling retail transaction data for personalized shopping recommendation
Authors: Pengfei Wang, Jiafeng Guo, Yanyan Lan
Venue: Conference CIKM 2014
Year: 2014

Retail transaction data conveys rich preference information on brands and goods from customers. How to mine the transaction data to provide personalized recommendation to customers becomes a critical task for retailers. Previous recommendation methods either focus on the user-product matrix and ignore the transactions, or only use the partial information of transactions, leading to inferior performance in recommendation. Inspired by association rule mining, we introduce association pattern as a basic unit to capture the correlation between products from both intra- and intertransactions. A Probabilistic model over the Association Patterns (PAP for short) is then employed to learn the potential shopping interests and also to provide personalized recommendations. Experimental results on two real world retail data sets show that our proposed method can outperform the state-of-the-art recommendation methods.
Modeling document networks with tree-averaged copula regularization
Authors: Yuan He, Cheng Wang, Changjun Jiang
Venue: Conference WSDM 2017
Year: 2017

Document network is a kind of intriguing dataset which provides both topical (texts) and topological (links) information. Most previous work assumes that documents closely linked with each other share common topics. However, the associations among documents are usually complex, which are not limited to the homophily (i.e., tendency to link to similar others). Actually, the heterophily (i.e., tendency to link to different others) is another pervasive phenomenon in social networks. In this paper, we introduce a new tool, called copula, to separately model the documents and links, so that different copula functions can be applied to capture different correlation patterns. In statistics, a copula is a powerful framework for explicitly modeling the dependence of random variables by separating the marginals and their correlations. Though widely used in Economics, copulas have not been paid enough attention to by researchers in machine learning field. Besides, to further capture the potential associations among the unconnected documents, we apply the tree-averaged copula instead of a single copula function. This improvement makes our model achieve better expressive power, and also more elegant in algebra. We derive efficient EM algorithms to estimate the model parameters, and evaluate the performance of our model on three different datasets. Experimental results show that our approach achieves significant improvements on both topic and link modeling compared with the current state of the art.
On measuring social friend interest similarities in recommender systems
Authors: Hao Ma
Venue: Conference SIGIR 2014
Year: 2014

Social recommender system has become an emerging research topic due to the prevalence of online social networking services during the past few years. In this paper, aiming at providing fundamental support to the research of social recommendation problem, we conduct an in-depth analysis on the correlations between social friend relations and user interest similarities. When evaluating interest similarities without distinguishing different friends a user has, we surprisingly observe that social friend relations generally cannot represent user interest similarities. A user's average similarity on all his/her friends is even correlated with the average similarity on some other randomly selected users. However, when measuring interest similarities using a finer granularity, we find that the similarities between a user and his/her friends are actually controlled by the network structure in the friend network. Factors that affect the interest similarities include subgraph topology, connected components, number of co-friends, etc. We believe our analysis provides substantial impact for social recommendation research and will benefit ongoing research in both recommender systems and other social applications.
Data summarization with social contexts
Authors: Hao Zhuang, Rameez Rahman, Xia Hu, Tian Guo, Pan Hui, Karl Aberer
Venue: Conference CIKM 2016
Year: 2016

While social data is being widely used in various applications such as sentiment analysis and trend prediction, its sheer size also presents great challenges for storing, sharing and processing such data. These challenges can be addressed by data summarization which transforms the original dataset into a smaller, yet still useful, subset. Existing methods find such subsets with objective functions based on data properties such as representativeness or informativeness but do not exploit social contexts, which are distinct characteristics of social data. Further, till date very little work has focused on topic preserving data summarization, despite the abundant work on topic modeling. This is a challenging task for two reasons. First, since topic model is based on latent variables, existing methods are not well-suited to capture latent topics. Second, it is difficult to find such social contexts that provide valuable information for building effective topic-preserving summarization model. To tackle these challenges, in this paper, we focus on exploiting social contexts to summarize social data while preserving topics in the original dataset. We take Twitter data as a case study. Through analyzing Twitter data, we discover two social contexts which are important for topic generation and dissemination, namely (i) CrowdExp topic score that captures the influence of both the crowd and the expert users in Twitter and (ii) Retweet topic score that captures the influence of Twitter users' actions. We conduct extensive experiments on two real-world Twitter datasets using two applications. The experimental results show that, by leveraging social contexts, our proposed solution can enhance topic-preserving data summarization and improve application performance by up to 18%.
Life-stage prediction for product recommendation in e-commerce
Authors: Peng Jiang, Yadong Zhu, Yi Zhang, Quan Yuan
Venue: Conference KDD 2015
Year: 2015

Although marketing researchers and sociologists have recognized the large impact of life stage on consumer's purchasing behaviors, existing recommender systems have not taken this impact into consideration. In this paper, we found obvious correlation between life stage and purchasing behavior in many E-commerce categories. For example, a mum may look for different suitable products when her baby is at different ages. Motivated by this, we introduce the conception of life stage into recommender systems and propose to predict a user's current life-stage and recommend products correspondingly. We propose a new Maximum Entropy Semi Markov Model to segment and label consumer life stage based on the observed purchasing data over time. In the mom-baby product category where the life stage transition is deterministic, we develop an efficient approximate solution using large scale logistic regression and a Viterbi-like algorithm. We also propose a Gaussian mixture model to efficiently handle multi-kids life stage prediction problem. We integrate the life stage information predicted into the recommender system behind the largest online shopping website taobao.com. Both offline and online experiments demonstrate the effectiveness of the proposed life-stage based recommendation approach.
Multiview clustering: a late fusion approach using latent models
Authors: Eric Bruno, Stéphane Marchand-Maillet
Venue: Conference SIGIR 2009
Year: 2009

Multi-view clustering is an important problem in information retrieval due to the abundance of data offering many perspectives and generating multi-view representations. We investigate in this short note a late fusion approach for multi-view clustering based on the latent modeling of cluster-cluster relationships. We derive a probabilistic multi-view clustering model outperforming an early-fusion approach based on multi-view feature correlation analysis.
Uncovering collusive spammers in chinese review websites
Authors: Chang Xu, Jie Zhang, Kuiyu Chang, Chong Long
Venue: Conference CIKM 2013
Year: 2013

As the rapid development of China's e-commerce in recent years and the underlying evolution of adversarial spamming tactics, more sophisticated spamming activities may carry out in Chinese review websites. Empirical analysis, on recently crawled product reviews from a popular Chinese e-commerce website, reveals the failure of many state-of-the-art spam indicators on detecting collusive spammers. Two novel methods are then proposed: 1) a KNN-based method that considers the pairwise similarity of two reviewers based on their group-level relational information and selects k most similar reviewers for voting; 2) a more general graph-based classification method that jointly classifies a set of reviewers based on their pairwise transaction correlations. Experimental results show that both our methods promisingly outperform the indicator-only classifiers in various settings.
Which to view: Personalized prioritization for broadcast emails
Authors: Beidou Wang, Martin Ester, Jiajun Bu, Yu Zhu, Ziyu Guan, Deng Cai
Venue: Conference WWW 2016
Year: 2016

Email is one of the most important communication tools today, but email overload resulting from the large number of unimportant or irrelevant emails is causing trillion-level economy loss every year. Thus personalized email prioritization algorithms are of urgent need. Despite lots of previous effort on this topic, broadcast email, an important type of email, is overlooked in previous literature. Broadcast emails are significantly different from normal emails, introducing both new challenges and opportunities. On one hand, lack of real senders and limited user interactions invalidate the key features exploited by traditional email prioritization algorithms; on the other hand, thousands of receivers for one broadcast email bring us the opportunity to predict importance through collaborative filtering. However, broadcast emails face a severe cold-start problem which hinders the direct application of collaborative filtering. In this paper, we propose the first framework for broadcast email prioritization by designing a novel active learning model that considers the collaborative filtering, implicit feedback and time sensitive responsiveness features of broadcast emails. Our method is thoroughly evaluated on a large scale real world industrial dataset from Samsung Electronics. Our method is proved highly effective and outperforms state-of-the-art personalized email prioritization methods.
The million domain challenge: Broadcast email prioritization by cross-domain recommendation
Authors: Beidou Wang, Martin Ester, Yikang Liao, Jiajun Bu, Yu Zhu, Ziyu Guan, Deng Cai
Venue: Conference KDD 2016
Year: 2016

With email overload becoming a billion-level drag on the economy, personalized email prioritization is of urgent need to help predict the importance level of an email. Despite lots of previous effort on the topic, broadcast email, an important type of emails with its unique challenges and intriguing opportunities, has been overlooked. The most salient opportunity lies in that effective collaborative filtering can be exploited due to thousands of receivers of a typical broadcast email. However, every broadcast email is completely cold and it is very costly to obtain users' preference feedback. Fortunately, there exist up to million-level broadcast mailing lists in a real life email system. Similar mailing lists can provide useful extra information for broadcast email prioritization in a target mailing list. How to mine such useful extra information is a challenging problem that has never been touched. In this work, we propose the first broadcast email prioritization framework considering large numbers of mailing lists by formulating this problem as a cross domain recommendation problem. An optimization framework is proposed to select the optimal set of source domains considering multiple criteria including overlap of users, feedback pattern similarity and coverage of users. Our method is thoroughly evaluated on a real world industrial dataset from Samsung Electronics and is proved highly effective and outperforms all the baselines.
Hallucinating faces: Tensorpatch super-resolution and coupled residue compensation
Authors: Wei Liu 0026, Dahua Lin, Xiaoou Tang
Venue: Conference CVPR 2005
Year: 2005

In this paper, we propose a new face hallucination framework based on image patches, which integrates two novel statistical super-resolution models. Considering that image patches reflect the combined effect of personal characteristics and patch-location, we first formulate a TensorPatch model based on multilinear analysis to explicitly model the interaction between multiple constituent factors. Motivated by locally linear embedding, we develop an enhanced multilinear patch hallucination algorithm, which efficiently exploits the local distribution structure in the sample space. To better preserve face subtle details, we derive the coupled PCA algorithm to learn the relation between high-resolution residue and low-resolution residue, which is utilized for compensate the error residue in hallucinated images. Experiments demonstrate that our framework on one hand well maintains the global facial structures, on the other hand recovers the detailed facial traits in high quality.