OpenRaiser/Intern-Atlas huggingface dataset:
Personalized response generation via domain adaptation
Authors: Min Yang, Zhou Zhao, Wei Zhao, Xiaojun Chen, Jia Zhu, Lianqiang Zhou, Zigang Cao
Venue: Conference SIGIR 2017
Year: 2017

In this paper, we propose a novel personalized response generation model via domain adaptation (PRG-DM). First, we learn the human responding style from large general data (without user-specific information). Second, we fine tune the model on a small size of personalized data to generate personalized responses with a dual learning mechanism. Moreover, we propose three new rewards to characterize good conversations that are personalized, informative and grammatical. We employ the policy gradient method to generate highly rewarded responses. Experimental results show that our model can generate better personalized responses for different users.
Unifying multi-source social media data for personalized travel route planning
Authors: Gang Hu, Jie Shao, Fumin Shen, Zi Huang, Heng Tao Shen
Venue: Conference SIGIR 2017
Year: 2017

Travel route planning aims to mine user's attributes and recommend personalized routes. How to build interest model for users and understand their real intention brings great challenges. This paper presents an approach which mines the user interest model by multi-source social media (e.g., travelogues and check-in records), and understands the user's real intention by active behavior such as point of interest (POI) inputs. In order to unify heterogeneous data from different sources, a topical package is built as the measurement space. Based on the topical package, user topical package is modeled to find user interest and route topical package is constructed to describe the attributes of each route. User's active behavior can also be considered during route planning, where top ranked routes are finally recommended. The proposed multi-source topical package (MSTP) approach is evaluated on a real dataset and compared with two state-of-the-art methods. The result shows that MSTP performs better for providing personalized travel routes.
A general model for out-of-town region recommendation
Authors: Tuan-Anh Nguyen Pham, Xutao Li, Gao Cong
Venue: Conference WWW 2017
Year: 2017

With the rapid growth of location-based social networks (LBSNs), it is now available to analyze and understand user mobility behavior in real world. Studies show that users usually visit nearby points of interest (POIs), located in small regions, especially when they travel out of their hometowns. However, previous out-of-town recommendation systems mainly focus on recommending individual POIs that may reside far from each other, which makes the recommendation results less useful. In this paper, we introduce a novel problem called Region Recommendation, which aims to recommend an out-of-town region of POIs that are likely to be visited by a user. The proximity characteristic of user mobility behavior implies that the probability of visiting one POI depends on those of nearby POIs. Thus, to make accurate region recommendation, our proposed model exploits the influence between POIs, instead of treating them individually. Moreover, to overcome the efficiency problem of searching the best region, we propose a sweeping line-based method, and subsequently an constant-bounded algorithm for better efficiency. Experiments on two real-world datasets demonstrate the improved effectiveness of our models over baseline methods and efficiency of the approximate algorithm.
Happiness is a choice: sentiment and activity-aware location recommendation
Authors: Jia Wang, Yungang Feng, Elham Naghizade, Lida Rashidi, Kwan Hui Lim, Kate E. Lee
Venue: Conference WWW 2018
Year: 2018

Studying large, widely spread Twitter data has laid the foundation for many novel applications from predicting natural disasters and epidemics to understanding urban dynamics. Recent studies have focused on exploring people's emotional response to their urban environment, e.g., green spaces versus built up areas, through analysing the sentiment of tweets within that area. Since green spaces have the capacity to improve citizen's well-being, we developed a system that is capable of recommending green spaces to users. Our system is unique in the sense that the recommendations are tailored with regard to users' preferred activity as well as the degree of positive sentiments in each green space. We show that the incoming flow of tweets can be used to refine the recommendations over time. Furthermore, We implemented a web-based, user-friendly interface to solicit user inputs and display recommendation results.
A fast deep learning model for textual relevance in biomedical information retrieval
Authors: Sunil Mohan, Nicolas Fiorini, Sun Kim, Zhiyong Lu
Venue: Conference WWW 2018
Year: 2018

Publications in the life sciences are characterized by a large technical vocabulary, with many lexical and semantic variations for expressing the same concept. Towards addressing the problem of relevance in biomedical literature search, we introduce a deep learning model for the relevance of a document's text to a keyword style query. Limited by a relatively small amount of training data, the model uses pre-trained word embeddings. With these, the model first computes a variable-length Delta matrix between the query and document, representing a difference between the two texts, which is then passed through a deep convolution stage followed by a deep feed-forward network to compute a relevance score. This results in a fast model suitable for use in an online search engine. The model is robust and outperforms comparable state-of-the-art deep learning approaches.
Improving retrieval of short texts through document expansion
Authors: Miles Efron, Peter Organisciak, Katrina Fenlon
Venue: Conference SIGIR 2012
Year: 2012

Collections containing a large number of short documents are becoming increasingly common. As these collections grow in number and size, providing effective retrieval of brief texts presents a significant research problem. We propose a novel approach to improving information retrieval (IR) for short texts based on aggressive document expansion. Starting from the hypothesis that short documents tend to be about a single topic, we submit documents as pseudo-queries and analyze the results to learn about the documents themselves. Document expansion helps in this context because short documents yield little in the way of term frequency information. However, as we show, the proposed technique helps us model not only lexical properties, but also temporal properties of documents. We present experimental results using a corpus of microblog (Twitter) data and a corpus of metadata records from a federated digital library. With respect to established baselines, results of these experiments show that applying our proposed document expansion method yields significant improvements in effectiveness. Specifically, our method improves the lexical representation of documents and the ability to let time influence retrieval.
What if bots feel moods?
Authors: Lisong Qiu, Yingwai Shiu, Pingping Lin, Ruihua Song, Yue Liu, Dongyan Zhao 0001, Rui Yan 0001
Venue: Conference SIGIR 2020
Year: 2020

For social bots, smooth emotional transitions are essential for delivering a genuine conversation experience to users. Yet, the task is challenging because emotion is too implicit and complicated to understand. Among previous studies in the domain of retrieval-based conversational model, they only consider the factors of semantic and functional dependencies of utterances. In this paper, to implement a more empathetic retrieval-based conversation system, we incorporate emotional factors into context-response matching from two aspects: 1) On top of semantic matching, we propose an emotion-aware transition network to model the dynamic emotional flow and enhance context-response matching in retrieval-based dialogue systems with learnt intrinsic emotion features through a multi-task learning framework; 2) We design several flexible controlling mechanisms to customize social bots in terms of emotion. Extensive experiments on two benchmark datasets indicate that the proposed model can effectively track the flow of emotions throughout a human-machine conversation and significantly improve response selection in dialogues over the state-of-the-art baselines. We also empirically validate the emotion-control effects of our proposed model on three different emotional aspects. Finally, we apply such functionalities to a real IoT application.
Dealing with interdependencies and uncertainty in multi-channel advertising campaigns optimization
Authors: Alessandro Nuara, Nicola Sosio, Francesco Trovò, Maria Chiara Zaccardi, Nicola Gatti, Marcello Restelli
Venue: Conference WWW 2019
Year: 2019

In 2017, Internet ad spending reached 209 billion USD worldwide, while, e.g., TV ads brought in 178 billion USD. An Internet advertising campaign includes up to thousands of sub-campaigns on multiple channels, e.g., search, social, display, whose parameters (bid and daily budget) need to be optimized every day, subject to a (cumulative) budget constraint. Such a process is often unaffordable for humans and its automation is crucial. As also shown by marketing funnel models, the sub-campaigns are usually interdependent, e.g., display ads induce awareness, increasing the number of impressions-and, thus, also the number of conversions-of search ads. This interdependence is widely exploited by humans in the optimization process, whereas, to the best of our knowledge, no algorithm takes it into account. In this paper, we provide the first model capturing the sub-campaigns interdependence. We also provide the IDIL algorithm, which, employing Granger Causality and Gaussian Processes, learns from past data, and returns an optimal stationary bid/daily budget allocation. We prove theoretical guarantees on the loss of IDIL w.r.t. the clairvoyant solution, and we show empirical evidence of its superiority in both realistic and real-world settings when compared with existing approaches.
Macaw: An extensible conversational information seeking platform
Authors: Hamed Zamani, Nick Craswell
Venue: Conference SIGIR 2020
Year: 2020

Conversational information seeking (CIS) has been recognized as a major emerging research area in information retrieval. Such research will require data and tools, to allow the implementation and study of conversational systems. This paper introduces Macaw, an open-source framework with a modular architecture for CIS research. Macaw supports multi-turn, multi-modal, and mixed-initiative interactions, and enables research for tasks such as document retrieval, question answering, recommendation, and structured data exploration. It has a modular design to encourage the study of new CIS algorithms, which can be evaluated in batch mode. It can also integrate with a user interface, which allows user studies and data collection in an interactive mode, where the back end can be fully algorithmic or a wizard of oz setup. Macaw is distributed under the MIT License.
Probabilistic and Dynamic Molecule-Disease Interaction Modeling for Drug Discovery
Authors: Tianfan Fu, Cao Xiao, Cheng Qian, Lucas M. Glass, Jimeng Sun
Venue: Conference KDD 2021
Year: 2021

Drug discovery aims at finding promising drug molecules for treating target diseases. Existing computational drug discovery methods mainly depend on molecule databases, ignoring valuable data collected from clinical trials. In this work, we propose PRIME to leverage high-quality drug molecules and drug-disease relations in historical clinical trials to narrow down the molecular search space in drug discovery. PRIME also introduces time dependency constraints to model evolving drug-disease relations using a probabilistic deep learning model that can quantify model uncertainty. We evaluated PRIME against leading models on both de novo design and drug repurposing tasks. Results show that compared with the best baselines, PRIME achieves 25.9% relative improvement (i.e., reduction) in average hit-ranking on drug repurposing and 47.6% relative improvement in success rate on de novo design.
Multi-view scene flow estimation: A view centered variational approach
Authors: Tali Basha, Yael Moses, Nahum Kiryati
Venue: Conference CVPR 2010
Year: 2010

We present a novel method for recovering the 3D structure and scene flow from calibrated multi-view sequences. We propose a 3D point cloud parametrization of the 3D structure and scene flow that allows us to directly estimate the desired unknowns. A unified global energy functional is proposed to incorporate the information from the available sequences and simultaneously recover both depth and scene flow. The functional enforces multi-view geometric consistency and imposes brightness constancy and piece-wise smoothness assumptions directly on the 3D unknowns. It inherently handles the challenges of discontinuities, occlusions, and large displacements. The main contribution of this work is the fusion of a 3D representation and an advanced variational framework that directly uses the available multi-view information. The minimization of the functional is successfully obtained despite the non-convex optimization problem. The proposed method was tested on real and synthetic data.
Incomplete follow-up question resolution using retrieval based sequence to sequence learning
Authors: Vineet Kumar, Sachindra Joshi
Venue: Conference SIGIR 2017
Year: 2017

Intelligent personal assistants (IPAs) and interactive question answering (IQA) systems frequently encounter incomplete follow-up questions. The incomplete follow-up questions only make sense when seen in conjunction with the conversation context: the previous question and answer. Thus, IQA and IPA systems need to utilize the conversation context in order to handle the incomplete follow-up questions and generate an appropriate response. In this work, we present a retrieval based sequence to sequence learning system that can generate the complete (or intended) question for an incomplete follow-up question (given the conversation context). We can train our system using only a small labeled dataset (with only a few thousand conversations), by decomposing the original problem into two simpler and independent problems. The first problem focuses solely on selecting the candidate complete questions from a library of question templates (built offline using the small labeled conversations dataset). In the second problem, we re-rank the selected candidate questions using a neural language model (trained on millions of unlabelled questions independently). Our system can achieve a BLEU score of 42.91, as compared to 29.11 using an existing generation based approach. We further demonstrate the utility of our system as a plug-in module to an existing QA pipeline. Our system when added as a plug-in module, enables Siri to achieve an improvement of 131.57% in answering incomplete follow-up questions.
You are who you know: inferring user profiles in online social networks
Authors: Alan Mislove, Bimal Viswanath, P. Krishna Gummadi, Peter Druschel
Venue: Conference WSDM 2010
Year: 2010

Online social networks are now a popular way for users to connect, express themselves, and share content. Users in today's online social networks often post a profile, consisting of attributes like geographic location, interests, and schools attended. Such profile information is used on the sites as a basis for grouping users, for sharing content, and for suggesting users who may benefit from interaction. However, in practice, not all users provide these attributes.
Unveiling a socio-economic system in a virtual world: a case study of an MMORPG
Authors: Selin Chun, Daejin Choi, Jinyoung Han, Huy Kang Kim, Taekyoung Kwon
Venue: Conference WWW 2018
Year: 2018

Understanding socio-economic systems in MMORPGs can provide an important implication on how people participate in the economy and how people interact with each other. In this paper, we model the socio-economic system of an Aion, a popular MMORPG, as a multi-layer graph. Using the dataset consisting of 94,870 users and their activity records spanning three months, we examine how economic activities are associated with social interactions, and find that social interactions like participating in a party or exchanging messages are highly correlated with the trade activities. We also find that virtual economy in Aion is heavily inclined to a small number of upper-class userswho play a crucial role in virtual economy. Our analysis on the upper-class users reveals that a significant portion of them reach at the max-level and tend to either (i) have many social interactions with others or (ii) play extremely much time with no social activity. We also reveal that there are some low-level upper-class users who gain much money but hardly socialize with others. Lastly, we show how upper-class users who are at low-levels, play the game extremely much more than others, or rarely interact with other users, are associated with the Real Money Trade (RMT), which may be an illegal behavior that gathers in-game money for exchanging into real-world money. We reveal that more than half of total money exchanged through the trade are associated with the upper-class users who involve in the RMT.
Predicting human mobility via variational attention
Authors: Qiang Gao, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, Fengli Zhang
Venue: Conference WWW 2019
Year: 2019

An important task in Location based Social Network applications is to predict mobility - specifically, user's next point-of-interest (POI) - challenging due to the implicit feedback of footprints, sparsity of generated check-ins, and the joint impact of historical periodicity and recent check-ins. Motivated by recent success of deep variational inference, we propose VANext (Variational Attention based Next) POI prediction: a latent variable model for inferring user's next footprint, with historical mobility attention. The variational encoding captures latent features of recent mobility, followed by searching the similar historical trajectories for periodical patterns. A trajectory convolutional network is then used to learn historical mobility, significantly improving the efficiency over often used recurrent networks. A novel variational attention mechanism is proposed to exploit the periodicity of historical mobility patterns, combined with recent check-in preference to predict next POIs. We also implement a semi-supervised variant - VANext-S, which relies on variational encoding for pre-training all current trajectories in an unsupervised manner, and uses the latent variables to initialize the current trajectory learning. Experiments conducted on real-world datasets demonstrate that VANext and VANext-S outperform the state-of-the-art human mobility prediction models.
Generative Adversarial Networks Enhanced Pre-training for Insufficient Electronic Health Records Modeling
Authors: Houxing Ren, Jingyuan Wang 0001, Wayne Xin Zhao
Venue: Conference KDD 2022
Year: 2022

In recent years, automatic computational systems based on deep learning are widely used in medical fields, such as automatic diagnosing and disease prediction. Most of these systems are designed for data sufficient scenarios. However, due to the disease rarity or privacy, the medical data are always insufficient. When applying these data-hungry deep learning models with insufficient data, it is likely to lead to issues of over-fitting and cause serious performance problems. Many data augmentation methods have been proposed to solve the data insufficiency problem, such as using GAN (Generative Adversarial Networks) to generate training data. However, the augmented data usually contains lots of noise. Directly using them to train sensitive medical models is very difficult to achieve satisfactory results.
Robust statistics on Riemannian manifolds via the geometric median
Authors: P. Thomas Fletcher, Suresh Venkatasubramanian, Sarang C. Joshi
Venue: Conference CVPR 2008
Year: 2008

The geometric median is a classic robust estimator of centrality for data in Euclidean spaces. In this paper we formulate the geometric median of data on a Riemannian manifold as the minimizer of the sum of geodesic distances to the data points. We prove existence and uniqueness of the geometric median on manifolds with non-positive sectional curvature and give sufficient conditions for uniqueness on positively curved manifolds. Generalizing the Weiszfeld procedure for finding the geometric median of Euclidean data, we present an algorithm for computing the geometric median on an arbitrary manifold. We show that this algorithm converges to the unique solution when it exists. This method produces a robust central point for data lying on a manifold, and should have use in a variety of vision applications involving manifolds. We give examples of the geometric median computation and demonstrate its robustness for three types of manifold data: the 3D rotation group, tensor manifolds, and shape spaces.
admscn: A novel perspective for user intent prediction in customer service bots
Authors: Kuan Xu, Chilin Fu, Xiaolu Zhang, Cen Chen, Ya-Lin Zhang, Wenge Rong, Zujie Wen, Jun Zhou, Xiaolong Li, Yu Qiao
Venue: Conference CIKM 2020
Year: 2020

As one of the core components of customer service bot, User Intent Prediction (UIP) aims at predicting users? intents (usually represented as predefined user questions) before they ask, and has been widely applied in real applications. However, when developing a machine learning system for this problem, two critical issues, i.e., the problem of feature drift and class imbalance, may emerge and seriously deprave the system performance. Moreover, various scenarios may arise due to business demands, making the aforementioned problems much more severe. To address these two problems, we propose an attention-based Deep Multi-instance Sequential Cross Network (aDMSCN) to deal with the UIP task. On the one hand,the UIP task can be subtly formalized as multi-instance learning(MIL) task with an attention-based method proposed to alleviate the influences of feature drift. To the best of our knowledge, this is the first attempt to model the problem from a MIL perspective.On the other hand, a ratio-sensitive loss is also developed in our model, which can mitigate the negative impact of class imbalance. Extensive experiments on both offline real-world datasets and on-line A/B testing show that our proposed framework significantly out performs other state-of-art methods for the UIP task.
Molecular feature mining in HIV data
Authors: Stefan Kramer, Luc De Raedt, Christoph Helma
Venue: Conference KDD 2001
Year: 2001

We present the application of Feature Mining techniques to the Developmental Therapeutics Program's AIDS antiviral screen database. The database consists of 43576 compounds, which were measured for their capability to protect human cells from HIV-1 infection. According to these measurements, the compounds were classified as either active, moderately active or inactive. The distribution of classes is extremely skewed: Only 1.3 % of the molecules is known to be active, and 2.7 % is known to be moderately active. Given this database, we were interested in molecular substructures (i.e., features) that are frequent in the active molecules, and infrequent in the inactives. In data mining terms, we focused on features with a minimum support in active compounds and a maximum support in inactive compounds. We analyzed the database using the levelwise version space algorithm that forms the basis of the inductive query and database system MOLFEA (Molecular Feature Miner). Within this framework, it is possible to declaratively specify the features of interest, such as the frequency of features on (possibly different) datasets as well as on the generality and syntax of them. Assuming that the detected substructures are causally related to biochemical mechanisms, it should be possible to facilitate the development of new pharmaceuticals with improved activities.