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Learning from Click Model and Latent Factor Model for Relevance Prediction Challenge
"... How to accurately interpret user click behaviour in search log is a key but challenging problem for search relevance. In this paper, we describe our solution to the relevance prediction challenge which achieves the first place among eligible teams. There are three stages in our solution: feature gen ..."
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How to accurately interpret user click behaviour in search log is a key but challenging problem for search relevance. In this paper, we describe our solution to the relevance prediction challenge which achieves the first place among eligible teams. There are three stages in our solution: feature generation, feature augmentation and learning a ranking function. In the first stage, we extract features in relation to querydocument pairs as well as individual queries and documents from the click log data. In the second stage, we induce additional features by click model techniques and learning latent factor models to correct different biases and discover the correlations between different queries or documents respectively. In the final stage, we apply supervised learning models on the limited labelled data to induce a model for predicting relevance based on the features generated in the previous two stages.
Rare Category Analysis
, 2010
"... In many real world problems, rare categories (minority classes) play an essential role despite of their extreme scarcity. For example, in financial fraud detection, the vast majority of the financial transactions are legitimate, and only a small number may be fraudulent; in Medicare fraud detection, ..."
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In many real world problems, rare categories (minority classes) play an essential role despite of their extreme scarcity. For example, in financial fraud detection, the vast majority of the financial transactions are legitimate, and only a small number may be fraudulent; in Medicare fraud detection, the percentage of bogus claims is small, but the total loss is significant; in network intrusion detection, malicious network activities are hidden among huge volumes of routine network traffic; in astronomy, only 0.001 % of the objects in sky survey images are truly beyond the scope of current science and may lead to new discoveries; in spam image detection, the nearduplicate spam images are difficult to discover from the large number of nonspam image; in rare disease diagnosis, the rare diseases affect less than 1 out of 2000 people, but the consequences can be very severe. Therefore, the discovery, characterization and prediction of rare categories or rare examples may protect us from fraudulent or malicious behaviors, provide the aid for scientific discoveries, and even save lives. This thesis focuses on rare category analysis, where the majority classes have a smooth distribution, and the minority classes exhibit a compactness property. Furthermore, we focus on the challenging cases where the support regions of the majority and minority classes overlap
Extracting Features from Ratings: The Role of Factor Models
"... Abstract. Performing effective preferencebased data retrieval requires detailed and preferentially meaningful structurized information about the current user as well as the items under consideration. A common problem is that representations of items often only consist of mere technical attributes, ..."
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Abstract. Performing effective preferencebased data retrieval requires detailed and preferentially meaningful structurized information about the current user as well as the items under consideration. A common problem is that representations of items often only consist of mere technical attributes, which do not resemble human perception. This is particularly true for integral items such as movies or songs. It is often claimed that meaningful item features could be extracted from collaborative rating data, which is becoming available through social networking services. However, there is only anecdotal evidence supporting this claim; but if it is true, the extracted information could very valuable for preferencebased data retrieval. In this paper, we propose a methodology to systematically check this common claim. We performed a preliminary investigation on a large collection of movie ratings and present initial evidence. 1
Bayesian nonnegative matrix factorization with stochastic variational inference
 Handbook of Mixed Membership Models and Their Applications. Chapman and Hall/CRC
, 2015
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Pattern Change Discovery between High Dimensional Data Sets
"... This paper investigates the general problem of pattern change discovery between highdimensional data sets. Current methods either mainly focus on magnitude change detection of lowdimensional data sets or are under supervised frameworks. In this paper, the notion of the principal angles between the ..."
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This paper investigates the general problem of pattern change discovery between highdimensional data sets. Current methods either mainly focus on magnitude change detection of lowdimensional data sets or are under supervised frameworks. In this paper, the notion of the principal angles between the subspaces is introduced to measure the subspace difference between two highdimensional data sets. Principal angles bear a property to isolate subspace change from the magnitude change. To address the challenge of directly computing the principal angles, we elect to use matrix factorization to serve as a statistical framework and develop the principle of the dominant subspace mapping to transfer the principal angle based detection to a matrix factorization problem. We show how matrix factorization can be naturally embedded into the likelihood ratio test based on the linear models. The proposed method is of an unsupervised nature and addresses the statistical significance of the pattern changes between highdimensional data sets. We have showcased the different applications of this solution in several specific realworld applications to demonstrate the power and effectiveness of this method.
Pack: Scalable parameterfree clustering on kpartite graphs
 In SDM Work. on Link Anal., Cntr.terror. and Secur
, 2009
"... Given an authorpaperconference graph, how can we automatically find groups for author, paper and conference respectively. Existing work either (1) requires fine tuning of several parameters, or (2) can only be applied to bipartite graphs (e.g., authorpaper graph, or paperconference graph). To ad ..."
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Given an authorpaperconference graph, how can we automatically find groups for author, paper and conference respectively. Existing work either (1) requires fine tuning of several parameters, or (2) can only be applied to bipartite graphs (e.g., authorpaper graph, or paperconference graph). To address this problem, in this paper, we propose PaCK for clustering such kpartite graphs. By optimizing an informationtheoretic criterion, PaCK searches for the best number of clusters for each type of object and generates the corresponding clustering. The unique feature of PaCK over existing methods for clustering kpartite graphs lies in its parameterfree nature. Furthermore, it can be easily generalized to the cases where certain connectivity relations are expressed as tensors, e.g., timeevolving data. The proposed algorithm is scalable in the sense that it is linear with respect to the total number of edges in the graphs. We present the theoretical analysis as well as the experimental evaluations to demonstrate both its effectiveness and efficiency. 1
Generalized Low Rank Models
, 2014
"... Principal components analysis (PCA) is a wellknown technique for approximating a data set represented by a matrix by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompa ..."
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Principal components analysis (PCA) is a wellknown technique for approximating a data set represented by a matrix by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well known techniques in data analysis, such as nonnegative matrix factorization, matrix completion, sparse and robust PCA, kmeans, kSVD, and maximum margin matrix factorization. The method handles heterogeneous data sets, and leads to coherent schemes for compressing, denoising, and imputing missing entries across all data types simultaneously. It also admits a number of interesting interpretations of the low rank factors, which allow clustering of examples or of features. We propose several parallel algorithms for fitting generalized low rank models, and describe implementations and numerical results. This manuscript is a draft. Comments sent to udell@stanford.edu are welcome.
Collaborative Competitive Filtering: . . .
, 2011
"... While a user’s preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learning recommender models. In particular, existing collaborative filtering (CF) approaches take into account only the binary eve ..."
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While a user’s preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learning recommender models. In particular, existing collaborative filtering (CF) approaches take into account only the binary events of user actions but totally disregard the contexts in which users ’ decisions are made. In this paper, we propose Collaborative Competitive Filtering (CCF), a framework for learning user preferences by modeling the choice process in recommender systems. CCF employs a multiplicative latent factor model to characterize the dyadic utility function. But unlike CF, CCF models the user behavior of choices by encoding a local competition effect. In this way, CCF allows us to leverage dyadic data that was previously lumped together with missing data in existing CF models. We present two formulations and an efficient large scale optimization algorithm. Experiments on three realworld recommendation data sets demonstrate that CCF significantly outperforms standard CF approaches in both offline and online evaluations.