OpenRaiser/Intern-Atlas huggingface dataset:
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects
Authors: Ziyu Guan, Jiajun Bu, Qiaozhu Mei, Chun Chen, Can Wang
Venue: Conference SIGIR 2009
Year: 2009

Social tagging is becoming increasingly popular in many Web 2.0 applications where users can annotate resources (e.g. Web pages) with arbitrary keywords (i.e. tags). A tag recommendation module can assist users in tagging process by suggesting relevant tags to them. It can also be directly used to expand the set of tags annotating a resource. The benefits are twofold: improving user experience and enriching the index of resources. However, the former one is not emphasized in previous studies, though a lot of work has reported that different users may describe the same concept in different ways. We address the problem of personalized tag recommendation for text documents. In particular, we model personalized tag recommendation as a "query and ranking" problem and propose a novel graph-based ranking algorithm for interrelated multi-type objects. When a user issues a tagging request, both the document and the user are treated as a part of the query. Tags are then ranked by our graph-based ranking algorithm which takes into consideration both relevance to the document and preference of the user. Finally, the top ranked tags are presented to the user as suggestions. Experiments on a large-scale tagging data set collected from Del.icio.us have demonstrated that our proposed algorithm significantly outperforms algorithms which fail to consider the diversity of different users' interests.
Hashtag recommendation for hyperlinked tweets
Authors: Surendra Sedhai, Aixin Sun
Venue: Conference SIGIR 2014
Year: 2014

Presence of hyperlink in a tweet is a strong indication of tweet being more informative. In this paper, we study the problem of hashtag recommendation for hyperlinked tweets (i.e., tweets containing links to Web pages). By recommending hashtags to hyperlinked tweets, we argue that the functions of hashtags such as providing the right context to interpret the tweets, tweet categorization, and tweet promotion, can be extended to the linked documents. The proposed solution for hashtag recommendation consists of two phases. In the first phase, we select candidate hashtags through five schemes by considering the similar tweets, the similar documents, the named entities contained in the document, and the domain of the link. In the second phase, we formulate the hashtag recommendation problem as a learning to rank problem and adopt RankSVM to aggregate and rank the candidate hashtags. Our experiments on a collection of 24 million tweets show that the proposed solution achieves promising results.
Savitr: A system for real-time location extraction from microblogs during emergencies
Authors: Ritam Dutt, Kaustubh Hiware, Avijit Ghosh, Rameshwar Bhaskaran
Venue: Conference WWW 2018
Year: 2018

We present SAVITR, a system that leverages the information posted on the Twitter microblogging site to monitor and analyse emergency situations. Given that only a very small percentage of microblogs are geo-tagged, it is essential for such a system to extract locations from the text of the microblogs. We employ natural language processing techniques to infer the locations mentioned in the microblog text, in an unsupervised fashion and display it on a map-based interface. The system is designed for efficient performance, achieving an F-score of 0.81, and is approximately two orders of magnitude faster than other available tools for location extraction.
Linking news across multiple streams for timeliness analysis
Authors: Ida Mele, Seyed Ali Bahrainian, Fabio Crestani
Venue: Conference CIKM 2017
Year: 2017

Linking multiple news streams based on the reported events and analyzing the streams' temporal publishing patterns are two very important tasks for information analysis, discovering newsworthy stories, studying the event evolution, and detecting untrustworthy sources of information. In this paper, we propose techniques for cross-linking news streams based on the reported events with the purpose of analyzing the temporal dependencies among streams.
Review sentiment-guided scalable deep recommender system
Authors: Dongmin Hyun, Chanyoung Park, Min-Chul Yang, Ilhyeon Song, Jung-Tae Lee, Hwanjo Yu
Venue: Conference SIGIR 2018
Year: 2018

Existing review-aware recommendation methods represent users (or items) through the concatenation of the reviews written by (or for) them, and depend entirely on convolutional neural networks (CNNs) to extract meaningful features for modeling users (or items). However, understanding reviews based only on the raw words of reviews is challenging because of the inherent ambiguity contained in them originated from the users' different tendency in writing. Moreover, it is inefficient in time and memory to model users/items by the concatenation of their associated reviews owing to considerably large inputs to CNNs. In this work, we present a scalable review-aware recommendation method, called SentiRec, that is guided to incorporate the sentiments of reviews when modeling the users and the items. SentiRec is a two-step approach composed of the first step that includes the encoding of each review into a fixed-size review vector that is trained to embody the sentiment of the review, followed by the second step that generates recommendations based on the vector-encoded reviews. Through our experiments, we show that SentiRec not only outperforms the existing review-aware methods, but also drastically reduces the training time and the memory usage. We also conduct a qualitative evaluation on the vector-encoded reviews trained by SentiRec to demonstrate that the overall sentiments are indeed encoded therein.
Embedding edge-attributed relational hierarchies
Authors: Muhao Chen, Chris Quirk
Venue: Conference SIGIR 2019
Year: 2019

Relational embedding methods encode objects and their relations as low-dimensional vectors. While achieving competitive performance on a variety of relational inference tasks, these methods fall short of preserving the hierarchies that are often formed in existing graph data, and ignore the rich edge attributes that describe the relation facts. In this paper, we propose a novel embedding method that simultaneously preserve the hierarchical property and the edge information in the edge-attributed relational hierarchies. The proposed method preserves the hierarchical relations by leveraging the non-linearity of hyperbolic vector translations, for which the edge attributes are exploited to capture the importance of each relation fact. Our experiment is conducted on the well-known Enron organizational chart, where the supervision relations between employees of the Enron company are accompanied with email-based attributes. We show that our method produces relational embeddings of higher quality than state-of-the-art methods, and outperforms a variety of strong baselines in reconstructing the organizational chart.
Multi-type itemset embedding for learning behavior success
Authors: Daheng Wang, Meng Jiang, Qingkai Zeng, Zachary Eberhart, Nitesh V. Chawla
Venue: Conference KDD 2018
Year: 2018

Contextual behavior modeling uses data from multiple contexts to discover patterns for predictive analysis. However, existing behavior prediction models often face difficulties when scaling for massive datasets. In this work, we formulate a behavior as a set of context items of different types (such as decision makers, operators, goals and resources), consider an observable itemset as a behavior success, and propose a novel scalable method, "multi-type itemset embedding", to learn the context items' representations preserving the success structures. Unlike most of existing embedding methods that learn pair-wise proximity from connection between a behavior and one of its items, our method learns item embeddings collectively from interaction among all multi-type items of a behavior, based on which we develop a novel framework, LearnSuc, for (1) predicting the success rate of any set of items and (2) finding complementary items which maximize the probability of success when incorporated into an itemset. Extensive experiments demonstrate both effectiveness and efficency of the proposed framework.
Domain transfer svm for video concept detection
Authors: Lixin Duan, Ivor Wai-Hung Tsang, Dong Xu 0001, Stephen J. Maybank
Venue: Conference CVPR 2009
Year: 2009

Cross-domain learning methods have shown promising results by leveraging labeled patterns from auxiliary domains to learn a robust classifier for target domain, which has a limited number of labeled samples. To cope with the tremendous change of feature distribution between different domains in video concept detection, we propose a new cross-domain kernel learning method. Our method, referred to as Domain Transfer SVM (DTSVM), simultaneously learns a kernel function and a robust SVM classifier by minimizing both the structural risk functional of SVM and the distribution mismatch of labeled and unlabeled samples between the auxiliary and target domains. Comprehensive experiments on the challenging TRECVID corpus demonstrate that DTSVM outperforms existing cross-domain learning and multiple kernel learning methods.
Emergency situation awareness from twitter for crisis management
Authors: Mark A. Cameron, Robert Power, Bella Robinson, Jie Yin
Venue: Conference WWW 2012
Year: 2012

This paper describes ongoing work with the Australian Government to detect, assess, summarise, and report messages of interest for crisis coordination published by Twitter. The developed platform and client tools, collectively termed the Emergency Situation Awareness - Automated Web Text Mining (ESA-AWTM) system, demonstrate how relevant Twitter messages can be identified and utilised to inform the situation awareness of an emergency incident as it unfolds.
Xiaoice band: A melody and arrangement generation framework for pop music
Authors: Hongyuan Zhu, Qi Liu, Nicholas Jing Yuan, Chuan Qin, Jiawei Li, Kun Zhang, Guang Zhou, Furu Wei, Yuanchun Xu, Enhong Chen
Venue: Conference KDD 2018
Year: 2018

With the development of knowledge of music composition and the recent increase in demand, an increasing number of companies and research institutes have begun to study the automatic generation of music. However, previous models have limitations when applying to song generation, which requires both the melody and arrangement. Besides, many critical factors related to the quality of a song such as chord progression and rhythm patterns are not well addressed. In particular, the problem of how to ensure the harmony of multi-track music is still underexplored. To this end, we present a focused study on pop music generation, in which we take both chord and rhythm influence of melody generation and the harmony of music arrangement into consideration. We propose an end-to-end melody and arrangement generation framework, called XiaoIce Band, which generates a melody track with several accompany tracks played by several types of instruments. Specifically, we devise a Chord based Rhythm and Melody Cross-Generation Model (CRMCG) to generate melody with chord progressions. Then, we propose a Multi-Instrument Co-Arrangement Model (MICA) using multi-task learning for multi-track music arrangement. Finally, we conduct extensive experiments on a real-world dataset, where the results demonstrate the effectiveness of XiaoIce Band.
Moliere: Automatic biomedical hypothesis generation system
Authors: Justin Sybrandt, Michael Shtutman, Ilya Safro
Venue: Conference KDD 2017
Year: 2017

Hypothesis generation is becoming a crucial time-saving technique which allows biomedical researchers to quickly discover implicit connections between important concepts. Typically, these systems operate on domain-specific fractions of public medical data. MOLIERE, in contrast, utilizes information from over 24.5 million documents and does not limit the document vocabulary. At the heart of our approach lies a multi-modal and multi-relational network of biomedical objects extracted from several heterogeneous datasets from the National Center for Biotechnology Information (NCBI). These objects include but are not limited to scientific papers, keywords, genes, proteins, diseases, and diagnoses. We model hypotheses using Latent Dirichlet Allocation applied on abstracts found near shortest paths discovered within this network. We demonstrate the effectiveness of MOLIERE by performing hypothesis generation on historical data. Our network, implementation, and resulting data are all publicly available for the broad scientific community.
Regularity and conformity: Location prediction using heterogeneous mobility data
Authors: Yingzi Wang, Nicholas Jing Yuan, Defu Lian, Linli Xu, Xing Xie, Enhong Chen, Yong Rui
Venue: Conference KDD 2015
Year: 2015

Mobility prediction enables appealing proactive experiences for location-aware services and offers essential intelligence to business and governments. Recent studies suggest that human mobility is highly regular and predictable. Additionally, social conformity theory indicates that people's movements are influenced by others. However, existing approaches for location prediction fail to organically combine both the regularity and conformity of human mobility in a unified model, and lack the capacity to incorporate heterogeneous mobility datasets to boost prediction performance. To address these challenges, in this paper we propose a hybrid predictive model integrating both the regularity and conformity of human mobility as well as their mutual reinforcement. In addition, we further elevate the predictive power of our model by learning location profiles from heterogeneous mobility datasets based on a gravity model. We evaluate the proposed model using several city-scale mobility datasets including location check-ins, GPS trajectories of taxis, and public transit data. The experimental results validate that our model significantly outperforms state-of-the-art approaches for mobility prediction in terms of multiple metrics such as accuracy and percentile rank. The results also suggest that the predictability of human mobility is time-varying, e.g., the overall predictability is higher on workdays than holidays while predicting users' unvisited locations is more challenging for workdays than holidays.
Predicting advertiser bidding behaviors in sponsored search by rationality modeling
Authors: Haifeng Xu, Bin Gao, Diyi Yang, Tie-Yan Liu
Venue: Conference WWW 2013
Year: 2013

We study how an advertiser changes his/her bid prices in sponsored search, by modeling his/her rationality. Predicting the bid changes of advertisers with respect to their campaign performances is a key capability of search engines, since it can be used to improve the offline evaluation of new advertising technologies and the forecast of future revenue of the search engine. Previous work on advertiser behavior modeling heavily relies on the assumption of perfect advertiser rationality; however, in most cases, this assumption does not hold in practice. Advertisers may be unwilling, incapable, and/or constrained to achieve their best response. In this paper, we explicitly model these limitations in the rationality of advertisers, and build a probabilistic advertiser behavior model from the perspective of a search engine. We then use the expected payoff to define the objective function for an advertiser to optimize given his/her limited rationality. By solving the optimization problem with Monte Carlo, we get a prediction of mixed bid strategy for each advertiser in the next period of time. We examine the effectiveness of our model both directly using real historical bids and indirectly using revenue prediction and click number prediction. Our experimental results based on the sponsored search logs from a commercial search engine show that the proposed model can provide a more accurate prediction of advertiser bid behaviors than several baseline methods.
Stereoscopic inpainting: Joint color and depth completion from stereo images
Authors: Liang Wang 0002, Hailin Jin, Ruigang Yang, Minglun Gong
Venue: Conference CVPR 2008
Year: 2008

We present a novel algorithm for simultaneous color and depth inpainting. The algorithm takes stereo images and estimated disparity maps as input and fills in missing color and depth information introduced by occlusions or object removal. We first complete the disparities for the occlusion regions using a segmentation-based approach. The completed disparities can be used to facilitate the user in labeling objects to be removed. Since part of the removed regions in one image is visible in the other, we mutually complete the two images through 3D warping. Finally, we complete the remaining unknown regions using a depth-assisted texture synthesis technique, which simultaneously fills in both color and depth. We demonstrate the effectiveness of the proposed algorithm on several challenging data sets.
Ontology summarization based on RDF sentence graph
Authors: Xiang Zhang, Gong Cheng, Yuzhong Qu
Venue: Conference WWW 2007
Year: 2007

Ontology summarization is very important to quick understanding and selection of ontologies. In this paper, we study extractive summarization of ontology. We propose a notion of RDF sentence as the basic unit of summarization. An RDF Sentence Graph is proposed to characterize the links between RDF sentences derived from a given ontology. The salience of each RDF sentence is assessed in terms of its "centrality" in the graph. We propose to summarize an ontology by extracting a set of salient RDF sentences according to a re-ranking strategy. We compare several measurements in assessing the salience of RDF sentences and give an overall evaluation of experiment results, which shows that our approach to ontology summarization is feasible.
Restful: Resolution-aware forecasting of behavioral time series data
Authors: Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Louis Faust, Nitesh V. Chawla
Venue: Conference CIKM 2018
Year: 2018

Leveraging historical behavioral data (e.g., sales volume and email communication) for future prediction is of fundamental importance for practical domains ranging from sales to temporal link prediction. Current forecasting approaches often use only a single time resolution (e.g., daily or weekly), which truncates the range of observable temporal patterns. However, real-world behavioral time series typically exhibit patterns across multi-dimensional temporal patterns, yielding dependencies at each level. To fully exploit these underlying dynamics, this paper studies the forecasting problem for behavioral time series data with the consideration of multiple time resolutions and proposes a multi-resolution time series forecasting framework, RESolution-aware Time series Forecasting (RESTFul). In particular, we first develop a recurrent framework to encode the temporal patterns at each resolution. In the fusion process, a convolutional fusion framework is proposed, which is capable of learning conclusive temporal patterns for modeling behavioral time series data to predict future time steps. Our extensive experiments demonstrate that the RESTFul model significantly outperforms the state-of-the-art time series prediction techniques on both numerical and categorical behavioral time series data.
Predicting matchups and preferences in context
Authors: Shuo Chen, Thorsten Joachims
Venue: Conference KDD 2016
Year: 2016

We present a general probabilistic framework for predicting the outcome of pairwise matchups (e.g. two-player sport matches) and pairwise preferences (e.g. product preferences), both of which have widespread applications ranging from matchmaking in computer games to recommendation in e-commerce. Unlike existing models for these tasks, our model not only learns representations of the items in a more expressive latent vector space, but also models how context modifies matchup and preference outcomes. For example, the context "weather" may alter the winning probability in a tennis match, or the fact that the user is on a mobile device may alter his preferences among restaurants. More generally, the model is capable of handling any symmetric game/comparison problem that can be described by vectorized player/item and game/context features. We provide a comprehensive evaluation of its predictive performance with real datasets from both domains to show its ability to predict preference and game outcomes more accurately than existing models. Furthermore, we demonstrate on synthetic datasets the expressiveness of the model when compared against theoretical limits.
Objective image fusion performance characterisation
Authors: Vladimir S. Petrovic, Costas S. Xydeas
Venue: Conference ICCV 2005
Year: 2005

Image fusion as a way of combining multiple image signals into a single fused image has in recent years been extensively researched for a variety of multisensor applications. Choosing an optimal fusion approach for each application from the plethora of algorithms available however, remains a largely open issue. A small number of metrics proposed so far provide only a rough, numerical estimate of fusion performance with limited understanding of the relative merits of different fusion schemes. This paper proposes a method for comprehensive, objective, image fusion performance characterisation using a fusion evaluation framework based on gradient information representation. The method provides an in-depth analysis of fusion performance by quantifying: information contributions by each sensor, fusion gain, fusion information loss and fusion artifacts (artificial information created). It is demonstrated on the evaluation of an extensive dataset of multisensor images fused with a wide range of established image fusion algorithms. The results demonstrate and quantify a number of well known issues concerning the performance of these schemes and provide a useful insight into a number of more subtle yet important fusion performance effects not immediately accessible to an observer.
GraphCAR: Content-aware multimedia recommendation with graph autoencoder
Authors: Qidi Xu, Fumin Shen, Li Liu, Heng Tao Shen
Venue: Conference SIGIR 2018
Year: 2018

Precisely recommending relevant multimedia items from massive candidates to a large number of users is an indispensable yet difficult task on many platforms. A promising way is to project users and items into a latent space and recommend items via the inner product of latent factor vectors. However, previous studies paid little attention to the multimedia content itself and couldn't make the best use of preference data like implicit feedback. To fill this gap, we propose a Content-aware Multimedia Recommendation Model with Graph Autoencoder (GraphCAR), combining informative multimedia content with user-item interaction. Specifically, user-item interaction, user attributes and multimedia contents (e.g., images, videos, audios, etc.) are taken as input of the autoencoder to generate the item preference scores for each user. Through extensive experiments on two real-world multimedia Web services: Amazon and Vine, we show that GraphCAR significantly outperforms state-of-the-art techniques of both collaborative filtering and content-based methods.
Robust object detection via soft cascade
Authors: Lubomir D. Bourdev, Jonathan Brandt
Venue: Conference CVPR 2005
Year: 2005

We describe a method for training object detectors using a generalization of the cascade architecture, which results in a detection rate and speed comparable to that of the best published detectors while allowing for easier training and a detector with fewer features. In addition, the method allows for quickly calibrating the detector for a target detection rate, false positive rate or speed. One important advantage of our method is that it enables systematic exploration of the ROC surface, which characterizes the trade-off between accuracy and speed for a given classifier.