OpenRaiser/Intern-Atlas huggingface dataset:
Probabilistic author-topic models for information discovery
Authors: Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, Thomas Griffiths
Venue: Conference KDD 2004
Year: 2004

We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words for that topic. The words in a multi-author paper are assumed to be the result of a mixture of each authors' topic mixture. The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm. We apply the methodology to a large corpus of 160,000 abstracts and 85,000 authors from the well-known CiteSeer digital library, and learn a model with 300 topics. We discuss in detail the interpretation of the results discovered by the system including specific topic and author models, ranking of authors by topic and topics by author, significant trends in the computer science literature between 1990 and 2002, parsing of abstracts by topics and authors and detection of unusual papers by specific authors. An online query interface to the model is also discussed that allows interactive exploration of author-topic models for corpora such as CiteSeer.
Multi-instance domain adaptation for vaccine adverse event detection
Authors: Junxiang Wang, Liang Zhao
Venue: Conference WWW 2018
Year: 2018

Detection of vaccine adverse events is crucial to the discovery and improvement of problematic vaccines. To achieve it, traditionally formal reporting systems like VAERS support accurate but delayed surveillance, while recently social media have been mined for timely but noisy observations. Utilizing the complementary strengths of these two domains to boost the detection performance looks good but cannot be effectively achieved by existing methods due to significant differences between their data characteristics, including: 1) formal language v.s. informal language, 2) single-message per user v.s. multi-messages per user, and 3) one class v.s. binary class. In this paper, we propose a novel generic framework named Multi-instance Domain Adaptation (MIDA) to maximize the synergy between these two domains in the vaccine adverse event detection task for social media users. Specifically, we propose a generalized Maximum Mean Discrepancy (MMD) criterion to measure the semantic distances between the heterogeneous messages from these two domains in their shared latent semantic space. Then these message-level generalized MMD distances are synthesized by newly proposed mixed instance kernels to user-level distances. We finally minimize the distances between the samples of the partially-matched classes from these two domains. In order to solve the non-convex optimization problem, an efficient Alternating Direction Method of Multipliers (ADMM) based algorithm combined with the Convex-Concave Procedure (CCP) is developed to optimize parameters accurately. Extensive experiments demonstrated that our model outperformed the baselines by a large margin under six metrics. Case studies showed that formal reports and extracted adverse-relevant tweets by MIDA shared a similarity of keyword and description patterns.
Accelerating dynamic time warping clustering with a novel admissible pruning strategy
Authors: Nurjahan Begum, Liudmila Ulanova, Jun Wang, Eamonn J. Keogh
Venue: Conference KDD 2015
Year: 2015

Clustering time series is a useful operation in its own right, and an important subroutine in many higher-level data mining analyses, including data editing for classifiers, summarization, and outlier detection. While it has been noted that the general superiority of Dynamic Time Warping (DTW) over Euclidean Distance for similarity search diminishes as we consider ever larger datasets, as we shall show, the same is not true for clustering. Thus, clustering time series under DTW remains a computationally challenging task. In this work, we address this lethargy in two ways. We propose a novel pruning strategy that exploits both upper and lower bounds to prune off a large fraction of the expensive distance calculations. This pruning strategy is admissible; giving us provably identical results to the brute force algorithm, but is at least an order of magnitude faster. For datasets where even this level of speedup is inadequate, we show that we can use a simple heuristic to order the unavoidable calculations in a most-useful-first ordering, thus casting the clustering as an anytime algorithm. We demonstrate the utility of our ideas with both single and multidimensional case studies in the domains of astronomy, speech physiology, medicine and entomology.
Exploring facial expressions with compositional features
Authors: Peng Yang 0001, Qingshan Liu 0001, Dimitris N. Metaxas
Venue: Conference CVPR 2010
Year: 2010

Most previous work focuses on how to learn discriminating appearance features over all the face without considering the fact that each facial expression is physically composed of some relative action units (AU). However, the definition of AU is an ambiguous semantic description in Facial Action Coding System (FACS), so it makes accurate AU detection very difficult. In this paper, we adopt a scheme of compromise to avoid AU detection, and try to interpret facial expression by learning some compositional appearance features around AU areas. We first divided face image into local patches according to the locations of AUs, and then we extract local appearance features from each patch. A minimum error based optimization strategy is adopted to build compositional features based on local appearance features, and this process embedded into Boosting learning structure. Experiments on the Cohn-Kanada database show that the proposed method has a promising performance and the built compositional features are basically consistent to FACS.
Centrality- Aware Link Recommendations
Authors: Nikos Parotsidis, Evaggelia Pitoura, Panayiotis Tsaparas
Venue: Conference WSDM 2016
Year: 2016

Link recommendations are critical for both improving the utility and expediting the growth of social networks. Most previous approaches focus on suggesting links that are highly likely to be adopted. In this paper, we add a different perspective to the problem by aiming at recommending links that also improve specific properties of the network. In particular, our goal is to recommend to users links that if adopted would improve their centrality in the network. Specifically, we introduce the centrality-aware link recommendation problem as the problem of recommending to a user u, k links from a pool of recommended links so as to maximize the expected decrease of the sum of the shortest path distances of $u$ to all other nodes in the network. We show that the problem is NP-hard, but our optimization function is monotone and sub-modular which guarantees a constant approximation ratio for the greedy algorithm. We present a fast algorithm for computing the expected decrease caused by a set of recommendations which we use as a building block in our algorithms. We provide experimental results that evaluate the performance of our algorithms with respect to both the accuracy of the prediction and the improvement in the centrality of the nodes, and we study the tradeoff between the two.
Annotating points of interest with geo-tagged tweets
Authors: Kaiqi Zhao, Gao Cong, Aixin Sun
Venue: Conference CIKM 2016
Year: 2016

Microblogging services like Twitter contain abundant of user generated content covering a wide range of topics. Many of the tweets can be associated to real-world entities for providing additional information for the latter. In this paper, we aim to associate tweets that are semantically related to real-world locations or Points of Interest (POIs). Tweets contain dynamic and real-time information while POIs contain relatively static information. The tweets associated with POIs provide complementary information for many applications like opinion mining and POI recommendation; the associated POIs can also be used as POI tags in Twitter. We define the research problem of annotating POIs with tweets and propose a novel supervised Bayesian Model (sBM). The model takes into account the textual, spatial features and user behaviors together with the supervised information of whether a tweet is POI-related. It is able to capture user interests in latent regions for the prediction of whether a tweet is POI-related and the association between the tweet and its most semantically related POI. On tweets and POIs collected for two cities (New York City and Singapore), we demonstrate the effectiveness of our models against baseline methods.
Spherical embeddings for non-euclidean dissimilarities
Authors: Richard C. Wilson 0001, Edwin R. Hancock, Elzbieta Pekalska, Robert P. W. Duin
Venue: Conference CVPR 2010
Year: 2010

Many computer vision and pattern recognition problems may be posed by defining a way of measuring dissimilarities between patterns. For many types of data, these dissimilarities are not Euclidean, and may not be metric. In this paper, we provide a means of embedding such data. We aim to embed the data on a hypersphere whose radius of curvature is determined by the dissimilarity data. The hypersphere can be either of positive curvature (elliptic) or of negative curvature (hyperbolic). We give an efficient method for solving the elliptic and hyperbolic embedding problems on symmetric dissimilarity data. This method gives the radius of curvature and a method for approximating the objects as points on a hyperspherical manifold. We apply our method to a variety of data including shape-similarities, graph-similarity and gesture-similarity data. In each case the embedding maintains the local structure of the data while placing the points in a metric space.
Personalized collaborative clustering
Authors: Yisong Yue, Chong Wang, Khalid El-Arini, Carlos Guestrin
Venue: Conference WWW 2014
Year: 2014

We study the problem of learning personalized user models from rich user interactions. In particular, we focus on learning from clustering feedback (i.e., grouping recommended items into clusters), which enables users to express similarity or redundancy between different items. We propose and study a new machine learning problem for personalization, which we call collaborative clustering. Analogous to collaborative filtering, in collaborative clustering the goal is to leverage how existing users cluster or group items in order to predict similarity models for other users' clustering tasks. We propose a simple yet effective latent factor model to learn the variability of similarity functions across a user population. We empirically evaluate our approach using data collected from a clustering interface we developed for a goal-oriented data exploration (or sensemaking) task: asking users to explore and organize attractions in Paris. We evaluate using several realistic use cases, and show that our approach learns more effective user models than conventional clustering and metric learning approaches.
An Attribute-Aware Neural Attentive Model for Next Basket Recommendation
Authors: Ting Bai, Jian-Yun Nie, Wayne Xin Zhao, Yutao Zhu, Pan Du, Ji-Rong Wen
Venue: Conference SIGIR 2018
Year: 2018

Next basket recommendation is a new type of recommendation, which recommends a set of items, or a basket, to the user. Purchase in basket is a common behavior of consumers. Recently, deep neural networks have been applied to model sequential transactions of baskets in next basket recommendation. However, current methods do not track the user's evolving appetite for items explicitly, and they ignore important item attributes such as product category. In this paper, we propose a novel Attribute-aware Neural Attentive Model (ANAM) to address these problems. ANAM adopts an attention mechanism to explicitly model user's evolving appetite for items, and utilizes a hierarchical architecture to incorporate the attribute information. In specific, ANAM utilizes a recurrent neural network to model the user's sequential behavior over time, and relays the user's appetite toward items and their attributes to next basket through attention weights shared across baskets on the two different hierarchies. Experiment results on two public datasets (ıe Ta-Feng and JingDong) demonstrate the effectiveness of our ANAM model for next basket recommendation.
Grocery shopping recommendations based on basket-sensitive random walk
Authors: Ming Li, M. Benjamin Dias, Ian H. Jarman, Wael El-Deredy, Paulo J. G. Lisboa
Venue: Conference KDD 2009
Year: 2009

We describe a recommender system in the domain of grocery shopping. While recommender systems have been widely studied, this is mostly in relation to leisure products (e.g. movies, books and music) with non-repeated purchases. In grocery shopping, however, consumers will make multiple purchases of the same or very similar products more frequently than buying entirely new items. The proposed recommendation scheme offers several advantages in addressing the grocery shopping problem, namely: 1) a product similarity measure that suits a domain where no rating information is available; 2) a basket sensitive random walk model to approximate product similarities by exploiting incomplete neighborhood information; 3) online adaptation of the recommendation based on the current basket and 4) a new performance measure focusing on products that customers have not purchased before or purchase infrequently. Empirical results benchmarking on three real-world data sets demonstrate a performance improvement of the proposed method over other existing collaborative filtering models.
Generating and personalizing bundle recommendations on steam
Authors: Apurva Pathak, Kshitiz Gupta, Julian J. McAuley
Venue: Conference SIGIR 2017
Year: 2017

Many websites offer promotions in terms of bundled items that can be purchased together, usually at a discounted rate. 'Bundling' may be a means of increasing sales revenue, but may also be a means for content creators to expose users to new items that they may not have considered in isolation. In this paper, we seek to understand the semantics of what constitutes a 'good' bundle, in order to recommend existing bundles to users on the basis of their constituent products, as well the more difficult task of generating new bundles that are personalized to a user. To do so we collect a new dataset from the Steam video game distribution platform, which is unique in that it contains both 'traditional' recommendation data (rating and purchase histories between users and items), as well as bundle purchase information. We assess issues such as bundle size and item compatibility, and show that these features, when combined with traditional matrix factorization techniques, can lead to highly effective bundle recommendation and generation.
Representing and recommending shopping baskets with complementarity, compatibility and loyalty
Authors: Mengting Wan, Di Wang, Jie Liu, Paul Bennett, Julian J. McAuley
Venue: Conference CIKM 2018
Year: 2018

We study the problem of representing and recommending products for grocery shopping. We carefully investigate grocery transaction data and observe three important patterns: products within the same basket complement each other in terms of functionality (complementarity); users tend to purchase products that match their preferences (compatibility); and a significant fraction of users repeatedly purchase the same products over time (loyalty). Unlike conventional e-commerce settings, complementarity and loyalty are particularly predominant in the grocery shopping domain. This motivates a new representation learning approach to leverage complementarity and compatibility holistically, as well as a new recommendation approach to explicitly account for users' 'must-buy' purchases in addition to their overall preferences and needs. Doing so not only improves product classification and recommendation performance on both public and proprietary transaction data covering various grocery store types, but also reveals interesting findings about the relationships between preferences, necessity, and loyalty in consumer purchases.
From chatter to headlines: harnessing the real-time web for personalized news recommendation
Authors: Gianmarco De Francisci Morales, Aristides Gionis, Claudio Lucchese
Venue: Conference WSDM 2012
Year: 2012

We propose a new methodology for recommending interesting news to users by exploiting the information in their twitter persona. We model relevance between users and news articles using a mix of signals drawn from the news stream and from twitter: the profile of the social neighborhood of the users, the content of their own tweet stream, and topic popularity in the news and in the whole twitter-land.
A location-based news article recommendation with explicit localized semantic analysis
Authors: Jeong Woo Son, A.-Yeong Kim, Seong-Bae Park
Venue: Conference SIGIR 2013
Year: 2013

The interest of users in handheld devices is strongly related to their location. Therefore, the user location is important, as a user context, for news article recommendation in a mobile environment. This paper proposes a novel news article recommendation that reflects the geographical context of the user. For this purpose, we propose the Explicit Localized Semantic Analysis (ELSA), an ESA-based topical representation of documents. Every location has its own geographical topics, which can be captured from the geo-tagged documents related to the location. Thus, not only news articles but locations are also represented as topic vectors. The main advantage of ELSA is that it stresses only the topics that are relevant to a given location, whereas all topics are equally important in ESA. As a result, geographical topics have different importance according to the user location in ELSA, even if they come from the same article. Another advantage of ELSA is that it allows a simple comparison of the user location and news articles, because it projects both locations and articles onto an identical space composed of Wikipedia topics. In the evaluation of ELSA with the New York Times corpus, it outperformed two simple baselines of Bag-Of-Words and LDA as well as two ESA-based methods. Rt10 of ELSA was improved up to 46.25% over other methods, and its [email protected] was always higher than those of the others regardless of k.
Neural demographic prediction using search query
Authors: Chuhan Wu, Fangzhao Wu, Junxin Liu, Shaojian He, Yongfeng Huang, Xing Xie
Venue: Conference WSDM 2019
Year: 2019

Demographics of online users such as age and gender play an important role in personalized web applications. However, it is difficult to directly obtain the demographic information of online users. Luckily, search queries can cover many online users and the search queries from users with different demographics usually have some difference in contents and writing styles. Thus, search queries can provide useful clues for demographic prediction. In this paper, we study predicting users' demographics based on their search queries, and propose a neural approach for this task. Since search queries can be very noisy and many of them are not useful, instead of combining all queries together for user representation, in our approach we propose a hierarchical user representation with attention (HURA) model to learn informative user representations from their search queries. Our HURA model first learns representations for search queries from words using a word encoder, which consists of a CNN network and a word-level attention network to select important words. Then we learn representations of users based on the representations of their search queries using a query encoder, which contains a CNN network to capture the local contexts of search queries and a query-level attention network to select informative search queries for demographic prediction. Experiments on two real-world datasets validate that our approach can effectively improve the performance of search query based age and gender prediction and consistently outperform many baseline methods.
Privacy preserving feature selection for distributed data using virtual dimension
Authors: Madhushri Banerjee, Sumit Chakravarty
Venue: Conference CIKM 2011
Year: 2011

Data Mining often suffers from the curse of dimensionality. Huge numbers of dimensions or attributes in the data pose serious problems to the data mining tasks. Traditionally data dimensionality reduction techniques like Principal Component Analysis have been used to address this problem.However, the need might be to remain in the original attribute space and identify the key predictive attributes instead of moving to a transformed space. As a result feature subset selection has become an important area of research over the last few years.
Top-n recommendation through belief propagation
Authors: Jiwoon Ha, Soon-Hyoung Kwon, Sang-Wook Kim, Christos Faloutsos, Sunju Park
Venue: Conference CIKM 2012
Year: 2012

The top-n recommendation focuses on finding the top-n items that the target user is likely to purchase rather than predicting his/her ratings on individual items. In this paper, we propose a novel method that provides top-n recommendation by probabilistically determining the target user's preference on items. This method models the purchasing relationships between users and items as a bipartite graph and employs Belief Propagation to compute the preference of the target user on items. We analyze the proposed method in detail by examining the changes in recommendation accuracy under different parameter settings. We also show that the proposed method is up to 40% more accurate than an existing method by comparing it with an RWR-based method via extensive experiments.
Crime rate inference with big data
Authors: Hongjian Wang, Daniel Kifer, Corina Graif, Zhenhui Li
Venue: Conference KDD 2016
Year: 2016

Crime is one of the most important social problems in the country, affecting public safety, children development, and adult socioeconomic status. Understanding what factors cause higher crime is critical for policy makers in their efforts to reduce crime and increase citizens' life quality. We tackle a fundamental problem in our paper: crime rate inference at the neighborhood level. Traditional approaches have used demographics and geographical influences to estimate crime rates in a region. With the fast development of positioning technology and prevalence of mobile devices, a large amount of modern urban data have been collected and such big data can provide new perspectives for understanding crime. In this paper, we used large-scale Point-Of-Interest data and taxi flow data in the city of Chicago, IL in the USA. We observed significantly improved performance in crime rate inference compared to using traditional features. Such an improvement is consistent over multiple years. We also show that these new features are significant in the feature importance analysis.
Arrival and departure dynamics in social networks
Authors: Shaomei Wu, Atish Das Sarma, Alex Fabrikant, Silvio Lattanzi, Andrew Tomkins
Venue: Conference WSDM 2013
Year: 2013

In this paper, we consider the natural arrival and departure of users in a social network, and ask whether the dynamics of arrival, which have been studied in some depth, also explain the dynamics of departure, which are not as well studied.