OpenRaiser/Intern-Atlas huggingface dataset:
Discovering temporal purchase patterns with different responses to promotions
Authors: Ling Luo, Bin Li, Irena Koprinska, Shlomo Berkovsky, Fang Chen
Venue: Conference CIKM 2016
Year: 2016

The supermarkets often use sales promotions to attract customers and create brand loyalty. They would often like to know if their promotions are effective for various customers, so that better timing and more suitable rate can be planned in the future. Given a transaction data set collected by an Australian national supermarket chain, in this paper we conduct a case study aimed at discovering customers' long-term purchase patterns, which may be induced by preference changes, as well as short-term purchase patterns, which may be induced by promotions. Since purchase events of individual customers may be too sparse to model, we propose to discover a number of latent purchase patterns from the data. The latent purchase patterns are modeled via a mixture of non-homogeneous Poisson processes where each Poisson intensity function is composed by long-term and short-term components. Through the case study, 1) we validate that our model can accurately estimate the occurrences of purchase events; 2) we discover easy-to-interpret long-term gradual changes and short-term periodic changes in different customer groups; 3) we identify the customers who are receptive to promotions through the correlation between behavior patterns and the promotions, which is particularly worthwhile for target marketing.
Online engagement for a healthier you: A case study of web-based supermarket health program
Authors: Ling Luo, Bin Li, Shlomo Berkovsky, Irena Koprinska, Fang Chen
Venue: Conference WWW 2017
Year: 2017

Obesity is a growing problem affecting millions of people. Various behavior change programs have been designed to reduce its prevalence. An Australian supermarket has recently run a web-based health program to motivate people to eat healthily and do more physical activity. The program offered discounts on fresh products and a website, HealthierU, providing interactive support tools for participants. The stakeholders desire to evaluate if the program is effective and if the supporting website is useful to facilitate behavior changes. To answer these questions, in this work we propose a method to: (1) model individual purchase rate from sparse recorded transactions through a mixture of Non-Homogeneous Poisson Processes (NHPP), (2) design criteria for partitioning participants based on their interactions with the HealthierU website, (3) evaluate the program impact by comparing behavior changes across different groups of participants. Our case study shows that during the program the participants significantly increased their purchases of some fresh products. Both the distribution of behavior patterns and impact scores show that the program imposed relatively strong impact on the participants who logged activities and tracked weights. Our method can facilitate the enhancement of personalized health programs, especially aiming to maximize the program impact and targeting participants through web or mobile applications.
Viewpoint invariant 3d landmark model inference from monocular 2d images using higher-order priors
Authors: Chaohui Wang, Yun Zeng, Loïc Simon, Ioannis A. Kakadiaris, Dimitris Samaras, Nikos Paragios
Venue: Conference ICCV 2011
Year: 2011

In this paper, we propose a novel one-shot optimization approach to simultaneously determine both the optimal 3D landmark model and the corresponding 2D projections without explicit estimation of the camera viewpoint, which is also able to deal with misdetections as well as partial occlusions. To this end, a 3D shape manifold is built upon fourth-order interactions of landmarks from a training set where pose-invariant statistics are obtained in this space. The 3D-2D consistency is also encoded in such high-order interactions, which eliminate the necessity of viewpoint estimation. Furthermore, the modeling of visibility improves further the performance of the method by handling missing correspondences and occlusions. The inference is addressed through a MAP formulation which is naturally transformed into a higher-order MRF optimization problem and is solved using a dual-decomposition-based method. Promising results on standard face benchmarks demonstrate the potential of our approach.
Deviation-based contextual SLIM recommenders
Authors: Yong Zheng, Bamshad Mobasher, Robin D. Burke
Venue: Conference CIKM 2014
Year: 2014

Context-aware recommender systems (CARS) help improve the effectiveness of recommendations by adapting to users' preferences in different contextual situations. One approach to CARS that has been shown to be particularly effective is Context-Aware Matrix Factorization (CAMF). CAMF incorporates contextual dependencies into the standard matrix factorization (MF) process, where users and items are represented as collections of weights over various latent factors. In this paper, we introduce another CARS approach based on an extension of matrix factorization, namely, the Sparse Linear Method (SLIM). We develop a family of deviation-based contextual SLIM (CSLIM) recommendation algorithms by learning rating deviations in different contextual conditions. Our CSLIM approach is better at explaining the underlying reasons behind contextual recommendations, and our experimental evaluations over five context-aware data sets demonstrate that these CSLIM algorithms outperform the state-of-the-art CARS algorithms in the top-N recommendation task. We also discuss the criteria for selecting the appropriate CSLIM algorithm in advance based on the underlying characteristics of the data.
Meta-Path-Based Ranking with Pseudo Relevance Feedback on Heterogeneous Graph for Citation Recommendation
Authors: Xiaozhong Liu, Yingying Yu, Chun Guo, Yizhou Sun
Venue: Conference CIKM 2014
Year: 2014

The sheer volume of scholarly publications available online significantly challenges how scholars retrieve the new information available and locate the candidate reference papers. While classical text retrieval and pseudo relevance feedback (PRF) algorithms can assist scholars in accessing needed publications, in this study, we propose an innovative publication ranking method with PRF by leveraging a number of meta-paths on the heterogeneous bibliographic graph. Different meta-paths on the graph address different ranking hypotheses, whereas the pseudo-relevant papers (from the retrieval results) are used as the seed nodes on the graph. Meanwhile, unlike prior studies, we propose "restricted meta-path" facilitated by a new context-rich heterogeneous network extracted from full-text publication content along with citation context. By using learning-to-rank, we integrate 18 different meta-path-based ranking features to derive the final ranking scores for candidate cited papers. Experimental results with ACM full-text corpus show that meta-path-based ranking with PRF on the new graph significantly (p < 0.0001) outperforms text retrieval algorithms with text-based or PageRank-based PRF.
QBEES: query by entity examples
Authors: Steffen Metzger, Ralf Schenkel, Marcin Sydow
Venue: Conference CIKM 2013
Year: 2013

Structured knowledge bases are an increasingly important way for storing and retrieving information. Within such knowledge bases, an important search task is finding similar entities based on one or more example entities. We present QBEES, a novel framework for defining entity similarity based only on structural features, so-called aspects, of the entities, that includes query-dependent and query-independent entity ranking components. We present evaluation results with a number of existing entity list completion benchmarks, comparing to several state-of-the-art baselines.
Single View Reconstruction Using Shape Grammars for Urban Environments
Authors: Panagiotis Koutsourakis, Loïc Simon, Olivier Teboul, Georgios Tziritas, Nikos Paragios
Venue: Conference ICCV 2009
Year: 2009

In this paper we introduce a novel approach to single view reconstruction using shape grammars. Our approach consists in modeling architectural styles using a set of basic shapes and a set of parametric rules, corresponding to increasing levels of detail. This approach is able to model elaborate and varying architectural styles, using a tree representation of variable depth and complexity. Towards reconstruction, the parameters of the rules are optimized using image-based and architectural costs. This is done through an efficient MRF formulation based on the shape grammar itself. The resulting framework can produce precise 3D models from single views, can deal with lack of texture and the presence of occlusions and specular reflections, while maintaining the ability to cope with very complex architectural styles. Promising results demonstrate the potential of our approach.
Cuckoo linear algebra
Authors: Li Zhou, David G. Andersen, Mu Li, Alexander J. Smola
Venue: Conference KDD 2015
Year: 2015

In this paper we present a novel data structure for sparse vectors based on Cuckoo hashing. It is highly memory efficient and allows for random access at near dense vector level rates. This allows us to solve sparse l1 programming problems exactly and without preprocessing at a cost that is identical to dense linear algebra both in terms of memory and speed. Our approach provides a feasible alternative to the hash kernel and it excels whenever exact solutions are required, such as for feature selection.
Human mobility synchronization and trip purpose detection with mixture of hawkes processes
Authors: Pengfei Wang, Yanjie Fu, Guannan Liu, Wenqing Hu, Charu C. Aggarwal
Venue: Conference KDD 2017
Year: 2017

While exploring human mobility can benefit many applications such as smart transportation, city planning, and urban economics, there are two key questions that need to be answered: (i) What is the nature of the spatial diffusion of human mobility across regions with different urban functions? (ii) How to spot and trace the trip purposes of human mobility trajectories? To answer these questions, we study large-scale and city-wide taxi trajectories; and furtherly organize them as arrival sequences according to the chronological arrival time. We figure out an important property across different regions from the arrival sequences, namely human mobility synchronization effect, which can be exploited to explain the phenomenon that two regions have similar arrival patterns in particular time periods if they share similar urban functions. In addition, the arrival sequences are mixed by arrival events with distinct trip purposes, which can be revealed by the regional environment of both the origins and destinations. To that end, in this paper, we develop a joint model that integrates Mixture of Hawkes Process (MHP) with a hierarchical topic model to capture the arrival sequences with mixed trip purposes. Essentially, the human mobility synchronization effect is encoded as a synchronization rate in the MHP; while the regional environment is modeled by introducing latent Trip Purpose and POI Topic to generate the Point of Interests (POIs) in the regions. Moreover, we provide an effective inference algorithm for parameter learning. Finally, we conduct intensive experiments on synthetic data and real-world data, and the experimental results have demonstrated the effectiveness of the proposed model.
Local algorithm for user action prediction towards display ads
Authors: Hongxia Yang, Yada Zhu, Jingrui He
Venue: Conference KDD 2017
Year: 2017

User behavior modeling is essential in computational advertisement, which builds users' profiles by tracking their online behaviors and then delivers the relevant ads according to each user's interests and needs. Accurate models will lead to higher targeting accuracy and thus improved advertising performance. Intuitively, similar users tend to have similar behaviors towards the displayed ads (e.g., impression, click, conversion). However, to the best of our knowledge, there is not much previous work that explicitly investigates such similarities of various types of user behaviors, and incorporates them into ad response targeting and prediction, largely due to the prohibitive scale of the problem.
Redqueen: An online algorithm for smart broadcasting in social networks
Authors: Ali Zarezade, Utkarsh Upadhyay, Hamid R. Rabiee, Manuel Gomez-Rodriguez
Venue: Conference WSDM 2017
Year: 2017

Users in social networks whose posts stay at the top of their followers' feeds the longest time are more likely to be noticed. Can we design an online algorithm to help them decide when to post to stay at the top? In this paper, we address this question as a novel optimal control problem for jump stochastic differential equations. For a wide variety of feed dynamics, we show that the optimal broadcasting intensity for any user is surprisingly simple ? it is given by the position of her most recent post on each of her follower's feeds. As a consequence, we are able to develop a simple and highly efficient online algorithm, RedQueen, to sample the optimal times for the user to post. Experiments on both synthetic and real data gathered from Twitter show that our algorithm is able to consistently make a user's posts more visible over time, is robust to volume changes on her followers' feeds, and significantly outperforms the state of the art.
The effectiveness of marketing strategies in social media: Evidence from promotional events
Authors: Panagiotis Adamopoulos, Vilma Todri
Venue: Conference KDD 2015
Year: 2015

This paper studies a novel social media venture and seeks to understand the effectiveness of marketing strategies in social media platforms by evaluating their impact on participating brands and organizations. We use a real-world data set and employ a promising research approach combining econometric with predictive modeling techniques in a causal estimation framework that allows for more accurate counterfactuals. Based on the results of the presented analysis and focusing on the long-term business value of marketing strategies in social media, we find that promotional events leveraging implicit or explicit advocacy in social media platforms result in significant abnormal returns for the participating brand, in terms of expanding the social media fan base of the firm. The effect is also economically significant as it corresponds to an increase of several thousand additional new followers per day for an average size brand. We also precisely quantify the impact of various promotion characteristics and demonstrate what types of promotions are more effective and for which brands, while suggesting specific tactical strategies. For instance, despite the competition for consumers' attention, brands and marketers should broadcast marketing messages on social networks during the time of peak usage in order to maximize their returns. Overall, we provide actionable insights with major implications for firms and social media platforms and contribute to the related literature as we discover new rich findings enabled by the employed causal estimation framework.
Hashtag retrieval in a microblogging environment
Authors: Miles Efron
Venue: Conference SIGIR 2010
Year: 2010

Microblog services let users broadcast brief textual messages to people who "follow" their activity. Often these posts contain terms called hashtags, markers of a post's meaning, audience, etc. This poster treats the following problem: given a user's stated topical interest, retrieve useful hashtags from microblog posts. Our premise is that a user interested in topic x might like to find hashtags that are often applied to posts about x. This poster proposes a language modeling approach to hashtag retrieval. The main contribution is a novel method of relevance feedback based on hashtags. The approach is tested on a corpus of data harvested from twitter.com.
Tagging your tweets: A probabilistic modeling of hashtag annotation in twitter
Authors: Zongyang Ma, Aixin Sun, Quan Yuan, Gao Cong
Venue: Conference CIKM 2014
Year: 2014

The adoption of hashtags in major social networks including Twitter, Facebook, and Google+ is a strong evidence of its importance in facilitating information diffusion and social chatting. To understand the factors (e.g., user interest, posting time and tweet content) that may affect hashtag annotation in Twitter and to capture the implicit relations between latent topics in tweets and their corresponding hashtags, we propose two PLSA-style topic models to model the hashtag annotation behavior in Twitter. Content-Pivoted Model (CPM) assumes that tweet content guides the generation of hashtags while Hashtag-Pivoted Model (HPM) assumes that hashtags guide the generation of tweet content. Both models jointly incorporate user, time, hashtag and tweet content in a probabilistic framework. The PLSA-style models also enable us to verify the impact of social factor on hashtag annotation by introducing social network regularization in the two models. We evaluate the proposed models using perplexity and demonstrate their effectiveness in two applications: retrospective hashtag annotation and related hashtag discovery. Our results show that HPM outperforms CPM by perplexity and both user and time are important factors that affect model performance. In addition, incorporating social network regularization does not improve model performance. Our experimental results also demonstrate the effectiveness of our models in both applications compared with baseline methods.
Gmove: Group-level mobility modeling using geo-tagged social media
Authors: Chao Zhang, Keyang Zhang, Quan Yuan, Luming Zhang, Tim Hanratty, Jiawei Han
Venue: Conference KDD 2016
Year: 2016

Understanding human mobility is of great importance to various applications, such as urban planning, traffic scheduling, and location prediction. While there has been fruitful research on modeling human mobility using tracking data (<i>e.g.</i>, GPS traces), the recent growth of geo-tagged social media (GeoSM) brings new opportunities to this task because of its sheer size and multi-dimensional nature. Nevertheless, how to obtain quality mobility models from the highly sparse and complex GeoSM data remains a challenge that cannot be readily addressed by existing techniques. We propose GMove, a <i>group-level mobility modeling</i> method using GeoSM data. Our insight is that the GeoSM data usually contains multiple user groups, where the users within the same group share significant movement regularity. Meanwhile, user grouping and mobility modeling are two intertwined tasks: (1) better user grouping offers better within-group data consistency and thus leads to more reliable mobility models; and (2) better mobility models serve as useful guidance that helps infer the group a user belongs to. GMove thus alternates between user grouping and mobility modeling, and generates an ensemble of Hidden Markov Models (HMMs) to characterize group-level movement regularity. Furthermore, to reduce text sparsity of GeoSM data, GMove also features a text augmenter. The augmenter computes keyword correlations by examining their spatiotemporal distributions. With such correlations as auxiliary knowledge, it performs sampling-based augmentation to alleviate text sparsity and produce high-quality HMMs. Our extensive experiments on two real-life data sets demonstrate that GMove can effectively generate meaningful group-level mobility models. Moreover, with context-aware location prediction as an example application, we find that GMove significantly outperforms baseline mobility models in terms of prediction accuracy.
Acrec: a co-authorship based random walk model for academic collaboration recommendation
Authors: Jing Li, Feng Xia, Wei Wang, Zhen Chen, Nana Yaw Asabere, Huizhen Jiang
Venue: Conference WWW 2014
Year: 2014

Recent academic procedures have depicted that work involving scientific research tends to be more prolific through collaboration and cooperation among researchers and research groups. On the other hand, discovering new collaborators who are smart enough to conduct joint-research work is accompanied with both difficulties and opportunities. One notable difficulty as well as opportunity is the big scholarly data. In this paper, we satisfy the demand of collaboration recommendation through co-authorship in an academic network. We propose a random walk model using three academic metrics as basics for recommending new collaborations. Each metric is studied through mutual paper co-authoring information and serves to compute the link importance such that a random walker is more likely to visit the valuable nodes. Our experiments on DBLP dataset show that our approach can improve the precision, recall rate and coverage rate of recommendation, compared with other state-of-the-art approaches.
Functional zone based hierarchical demand prediction for bike system expansion
Authors: Junming Liu, Leilei Sun, Qiao Li, Jingci Ming, Yanchi Liu, Hui Xiong
Venue: Conference KDD 2017
Year: 2017

Bike sharing systems, aiming at providing the missing links in public transportation systems, are becoming popular in urban cities. Many providers of bike sharing systems are ready to expand their bike stations from the existing service area to surrounding regions. A key to success for a bike sharing systems expansion is the bike demand prediction for expansion areas. There are two major challenges in this demand prediction problem: First. the bike transition records are not available for the expansion area and second. station level bike demand have big variances across the urban city. Previous research efforts mainly focus on discovering global features, assuming the station bike demands react equally to the global features, which brings large prediction error when the urban area is large and highly diversified. To address these challenges, in this paper, we develop a hierarchical station bike demand predictor which analyzes bike demands from functional zone level to station level. Specifically, we first divide the studied bike stations into functional zones by a novel Bi-clustering algorithm which is designed to cluster bike stations with similar POI characteristics and close geographical distances together. Then, the hourly bike check-ins and check-outs of functional zones are predicted by integrating three influential factors: distance preference, zone-to-zone preference, and zone characteristics. The station demand is estimated by studying the demand distributions among the stations within the same functional zone. Finally, the extensive experimental results on the NYC Citi Bike system with two expansion stages show the advantages of our approach on station demand and balance prediction for bike sharing system expansions.
Isis: A networked-epidemiology based pervasive web app for infectious disease pandemic planning and response
Authors: Richard J. Beckman, Keith R. Bisset, Jiangzhuo Chen, Bryan L. Lewis, Madhav V. Marathe, Paula Elaine Stretz
Venue: Conference KDD 2014
Year: 2014

We describe ISIS, a high-performance-computing-based application to support computational epidemiology of infectious diseases. ISIS has been developed over the last seven years in close coordination with public health and policy experts. It has been used in a number of important federal planning and response exercises. ISIS grew out of years of experience in developing and using HPC-oriented models of complex socially coupled systems. This identified the guiding principle that complex models will be used by domain experts only if they can do realistic analysis without becoming computing experts.