OpenRaiser/Intern-Atlas huggingface dataset:
Inferring Passengers' Interactive Choices on Public Transits via MA-AL: Multi-Agent Apprenticeship Learning
Authors: Mingzhou Yang, Yanhua Li, Xun Zhou, Hui Lu, Zhihong Tian, Jun Luo
Venue: WWW
Year: 2020

Public transports, such as subway lines and buses, offer affordable ride-sharing services and reduce the road network traffic. Extracting passengers’ preferences from their public transit choices is important to city planners but technically non-trivial. When traveling by taking public transits, passengers make sequences of transit choices, and their rewards are usually influenced by other passengers’ choices. This process can be modeled as a Markov Game (MG). In this paper, we make the first effort to model travelers’ preferences of making transit choices using MGs. Based on the discovery that passengers usually do not change their policies, we propose novel algorithms to extract reward functions from the observed deterministic equilibrium joint policy of all agents in a general-sum MG to infer travelers’ preferences. First, we assume we have the access to the entire joint policy. We characterize the set of all reward functions for which the given joint policy is a Nash equilibrium policy. In order to remove the degeneracy of the solution, we then attempt to pick reward functions so as to maximize the sum of the deviation between the the observed policy and the sub-optimal policy of each agent. This results in a skillfully solvable linear programming algorithm for the multi-agent inverse reinforcement learning (MA-IRL) problem. Then, we deal with the case where we have access to the equilibrium joint policy through a set of actual trajectories. We propose an iterative algorithm inspired by single-agent apprenticeship learning algorithms and the cyclic coordinate descent approach. We evaluate the proposed algorithms on both a simple Grid Game and a unique real-world dataset (from Shenzhen, China). Results show that when we have access to the full policy, our algorithm can efficiently recover most of the reward structure, especially the interaction of agents. In the case where we only have access to a set of sampled expert trajectories, our algorithm can provide an explanation of the expert trajectories. Measured with respect to the experts’ unknown reward function, the performance of the policy output by our algorithm is close to that of the expert policy.
Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations
Authors: Ji Yang, Xinyang Yi, Derek Zhiyuan Cheng, Lichan Hong, Yang Li, Simon Xiaoming Wang, Taibai Xu, Ed H. Chi
Venue: WWW
Year: 2020

Learning query and item representations is important for building large scale recommendation systems. In many real applications where there is a huge catalog of items to recommend, the problem of efficiently retrieving top k items given user’s query from deep corpus leads to a family of factorized modeling approaches where queries and items are jointly embedded into a low-dimensional space. In this paper, we first showcase how to apply a two-tower neural network framework, which is also known as dual encoder in the natural language community, to improve a large-scale, production app recommendation system. Furthermore, we offer a novel negative sampling approach called Mixed Negative Sampling (MNS). In particular, different from commonly used batch or unigram sampling methods, MNS uses a mixture of batch and uniformly sampled negatives to tackle the selection bias of implicit user feedback. We conduct extensive offline experiments using large-scale production dataset and show that MNS outperforms other baseline sampling methods. We also conduct online A/B testing and demonstrate that the two-tower retrieval model based on MNS significantly improves retrieval quality by encouraging more high-quality app installs.
A Multi-task Learning Framework for Road Attribute Updating via Joint Analysis of Map Data and GPS Traces
Authors: Yifang Yin, Jagannadan Varadarajan, Guanfeng Wang, Xueou Wang, Dhruva Sahrawat, Roger Zimmermann, See-Kiong Ng
Venue: WWW
Year: 2020

The quality of a digital map is of utmost importance for geo-aware services. However, maintaining an accurate and up-to-date map is a highly challenging task that usually involves a substantial amount of manual work. To reduce the manual efforts, methods have been proposed to automatically derive road attributes by mining GPS traces. However, previous methods always modeled each road attribute separately based on intuitive hand-crafted features extracted from GPS traces. This observation motivates us to propose a machine learning based method to learn joint features not only from GPS traces but also from map data. To model the relations among the target road attributes, we extract low-level shared feature embeddings via multi-task learning, while still being able to generate task-specific fused representations by applying attention-based feature fusion. To model the relations between the target road attributes and other contextual information that is available from a digital map, we propose to leverage map tiles at road centers as visual features that capture the information of the surrounding geographic objects around the roads. We perform extensive experiments on the OpenStreetMap where state-of-the-art classification accuracy has been obtained compared to existing road attribute detection approaches.
The Automated Copywriter: Algorithmic Rephrasing of Health-Related Advertisements to Improve their Performance
Authors: Brit Youngmann, Elad Yom-Tov, Ran Gilad-Bachrach, Danny Karmon
Venue: WWW
Year: 2020

Search advertising is one of the most commonly-used methods of advertising. Past work has shown that search advertising can be employed to improve health by eliciting positive behavioral change. However, writing effective advertisements requires expertise and (possible expensive) experimentation, both of which may not be available to public health authorities wishing to elicit such behavioral changes, especially when dealing with a public health crises such as epidemic outbreaks. Here we develop an algorithm which builds on past advertising data to train a sequence-to-sequence Deep Neural Network which “translates” advertisements into optimized ads that are more likely to be clicked. The network is trained using more than 114 thousands ads shown on Microsoft Advertising. We apply this translator to two health related domains: Medical Symptoms (MS) and Preventative Healthcare (PH) and measure the improvements in click-through rates (CTR). Our experiments show that the generated ads are predicted to have higher CTR in 81% of MS ads and 76% of PH ads. To understand the differences between the generated ads and the original ones we develop estimators for the affective attributes of the ads. We show that the generated ads contain more calls-to-action and that they reflect higher valence (36% increase) and higher arousal (87%) on a sample of 1000 ads. Finally, we run an advertising campaign where 10 random ads and their rephrased versions from each of the domains are run in parallel. We show an average improvement in CTR of 68% for the generated ads compared to the original ads. Our results demonstrate the ability to automatically optimize advertisement for the health domain. We believe that our work offers health authorities an improved ability to help nudge people towards healthier behaviors while saving the time and cost needed to optimize advertising campaigns.
Social Attentive Network for Live Stream Recommendation
Authors: Dung-Ru Yu, Chiao-Chuan Chu, Hsu-Chao Lai, Jiun-Long Huang
Venue: WWW
Year: 2020

Live streaming platforms not only provide live videos but also allow social interactions between viewers via real-time chatting. However, none of existing research has studied the social impact for recommending live streams. In this work, we formulate a new personalized recommendation problem by factoring in both video and social contents (chats). Accordingly, we 1) design a new attention network ANSWER to identify viewers’ attention on video and social contents, and 2) rank the channels based on the attentive features. We collect a real dataset from Twitch for evaluation. The experimental results manifest that ANSWER outperforms baselines by at least 26.6% in terms of NDCG@5.
Generating Multi-hop Reasoning Questions to Improve Machine Reading Comprehension
Authors: Jianxing Yu, Xiaojun Quan, Qinliang Su, Jian Yin
Venue: WWW
Year: 2020

This paper focuses on the topic of multi-hop question generation, which aims to generate questions needed reasoning over multiple sentences and relations to derive answers. In particular, we first build an entity graph to integrate various entities scattered over text based on their contextual relations. We then heuristically extract the sub-graph by the evidential relations and type, so as to obtain the reasoning chain and textual related contents for each question. Guided by the chain, we propose a holistic generator-evaluator network to form the questions, where such guidance helps to ensure the rationality of generated questions which need multi-hop deduction to correspond to the answers. The generator is a sequence-to-sequence model, designed with several techniques to make the questions syntactically and semantically valid. The evaluator optimizes the generator network by employing a hybrid mechanism combined of supervised and reinforced learning. Experimental results on HotpotQA data set demonstrate the effectiveness of our approach, where the generated samples can be used as pseudo training data to alleviate the data shortage problem for neural network and assist to learn the state-of-the-arts for multi-hop machine comprehension.
A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems
Authors: Ye Yuan, Xin Luo, Mingsheng Shang, Di Wu
Venue: WWW
Year: 2020

Recommender systems (RSs) commonly describe its user-item preferences with a high-dimensional and sparse (HiDS) matrix filled with non-negative data. A non-negative latent factor (NLF) model relying on a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is frequently adopted to process such an HiDS matrix. However, an NLF model mostly adopts Euclidean distance for its objective function, which is naturally a special case of α-β-divergence. Moreover, it frequently suffers slow convergence. For addressing these issues, this study proposes a generalized and fast-converging non-negative latent factor (GFNLF) model. Its main idea is two-fold: a) adopting α-β-divergence for its objective function, thereby enhancing its representation ability for HiDS data; b) deducing its momentum-incorporated non-negative multiplicative update (MNMU) algorithm, thereby achieving its fast convergence. Empirical studies on two HiDS matrices emerging from real RSs demonstrate that with carefully-tuned hyperparameters, a GFNLF model outperforms state-of-the-art models in both computational efficiency and prediction accuracy for missing data of an HiDS matrix.
Dynamic VNF Placement for Mapping Service Function Chain Requests in NFV-enabled Networks
Authors: Yi Yue, Bo Cheng, Xuan Liu, Meng Wang, Biyi Li
Venue: WWW
Year: 2020

Network function virtualization (NFV) brings significant performance and management benefits for the enterprises to place virtual network functions (VNFs) while reducing the operating expenses and capital expenditures. In this paper, we study the dynamic VNF placement problem for mapping users’ service function requests (SFCRs) in NFV-enabled networks. The problem is formulated as a heuristic cost function aiming at minimizing the cost of mapping each SFCR. Then we propose SFCR-Mapping and Dynamic VNF-Releasing algorithms to efficiently map SFCRs and timely release the redundant VNF instances. The simulation results demonstrate that our method efficiently reduces the total activated VNF instances and improves VNF utilization compared with the benchmarks.
FairSearch: A Tool For Fairness in Ranked Search Results
Authors: Meike Zehlike, Tom Sühr, Carlos Castillo, Ivan Kitanovski
Venue: WWW
Year: 2020

Ranked search results and recommendations have become the main mechanism by which we find content, products, places, and people online. With hiring, selecting, purchasing, and dating being increasingly mediated by algorithms, rankings may determine business opportunities, education, access to benefits, and even social success. It is therefore of societal and ethical importance to ask whether search results can demote, marginalize, or exclude individuals of unprivileged groups or promote products with undesired features. In this paper we present FairSearch, the first fair open source search API to provide fairness notions in ranked search results. We implement two well-known algorithms from the literature, namely FA*IR (Zehlike et al., 9) and DELTR (Zehlike and Castillo, 10) and provide them as stand-alone libraries in Python and Java. Additionally we implement interfaces to Elasticsearch for both algorithms, a well-known search engine API based on Apache Lucene. The interfaces use the aforementioned Java libraries and enable search engine developers who wish to ensure fair search results of different styles to easily integrate DELTR and FA*IR into their existing Elasticsearch environment.
Dynamic Network Modeling from Motif-Activity
Authors: Giselle Zeno, Timothy La Fond, Jennifer Neville
Venue: WWW
Year: 2020

Graph structure in dynamic networks changes rapidly. Using temporal information about their connections, models for dynamic networks can be developed and used to understand the process of how their structure changes over time. Additionally, higher-order motifs have been established as building blocks for the structure of networks. In this paper, we first demonstrate empirically in three dynamic network datasets, that motifs with edges: (1) do not transition from one motif type to another (e.g, wedges becoming triangles and vice-versa); (2) motifs re-appear in other time periods and the rate depends on their configuration. We propose the Dynamic Motif-Activity Model (DMA) for sampling synthetic dynamic graphs with parameters learned from an observed network. We evaluate our DMA model, with two dynamic graph generative model baselines, by measuring different graph structure metrics in the generated synthetic graphs and comparing with the graph used as input. Our results show that employing motifs captures the underlying graph structure and modeling their activity recreates the fast changes seen in dynamic networks.
TaxAA: a Reliable Tax Auditor Assistant for Exploring Suspicious Transactions
Authors: Zhichao Zha
Venue: WWW
Year: 2020

We present TaxAA, a reliable tax auditor assistant that helps tax auditors explore suspicious transactions and get reliable evidence. We construct a Tax Audit Network(TAN) and use extended algorithms based on the semi-supervised Graph Convolutional Network(GCN) to build detection models for calculating taxpayers’ suspicion score, we choose Hierarchical Graph Convolutional Network(H-GCN) as our basic model according to the experimental results. Then the visual analytic system allows tax auditors to customize suspicious indicators to observe the suspicious relationships among taxpayers by the ”wheel” chart. Meanwhile, it can provide detailed transactions and individual information for reference. We have evaluated the assistant based on the tax data of a province, our detection model can achieve high accuracy and the visual analytic system can provide useful guidance for tax auditors.
Mobile Cyber-Physical Systems for Smart Cities
Authors: Desheng Zhang
Venue: WWW
Year: 2020

Nowadays, rapid urbanization leads to severe urban challenges, e.g., congestion and energy consumption, related to human mobility. To address them, it is essential to (i) measure and predict human mobility based on data from urban infrastructure, and (ii) intervene and alter human mobility with novel services on urban infrastructure, i.e., a Cyber-Physical System approach. However, both existing mobility models and resultant services are built upon the interaction of residents with single infrastructure (e.g., taxis) or multiple infrastructures from single domains (e.g., transportation), which are limited by their homogeneous nature. Fortunately, cross-domain infrastructures and their data from the latest infrastructure expansion enable us to explore real-time interactions between residents and infrastructures across domains. In this talk, we will introduce some of our recent work under cross-domain interactions.
Learning Temporal Interaction Graph Embedding via Coupled Memory Networks
Authors: Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhao Li, Can Wang
Venue: WWW
Year: 2020

Graph embedding has become the research focus in both academic and industrial communities due to its powerful capabilities. The majority of existing work overwhelmingly learn node embeddings in the context of static, plain or attributed, homogeneous graphs. However, many real-world applications frequently involve bipartite graphs with temporal and attributed interaction edges, named temporal interaction graphs. The temporal interactions usually imply different facets of interest and might even evolve over time, thus putting forward huge challenges in learning effective node representations. In this paper, we propose a novel framework named TigeCMN to learn node representations from a sequence of temporal interactions. Specifically, we devise two coupled memory networks to store and update node embeddings in external matrices explicitly and dynamically, which forms deep matrix representations and could enhance the expressiveness of the node embeddings. We conduct experiments on two real-world datasets and the experimental results empirically demonstrate that TigeCMN can outperform the state-of-the-arts with different gains.
A Knowledge-Enriched Model for Emotional Conversation Generation
Authors: Ao Zhang, Shizhan Chen, Xiaowang Zhang, Rui Li, Xinzhi Zhang
Venue: WWW
Year: 2020

In this poster, we propose a knowledge-enriched emotional conversation generation model (KE-EGM) that can ensure high quality content and focus on the impact of emotional factors during the conversation. First, we apply a multi-embedding fusion layer to provide this model with the token-level and sentence-level understanding. Then, the emotion flow attention mechanism combines flow emotion state and attention mechanism to learn and capture emotional information during the conversation dynamically. Finally, the multi-objective optimization mechanism is introduced to detect and generate fine-grained emotional responses. The experimental results show that KE-EGM outperforms several baselines not only in the content aspect but also in the emotional aspect.
PG2S+: Stack Distance Construction Using Popularity, Gap and Machine Learning
Authors: Jiangwei Zhang, Y. C. Tay
Venue: WWW
Year: 2020

Stack distance characterizes temporal locality of workloads and plays a vital role in cache analysis since the 1970s. However, exact stack distance calculation is too costly, and impractical for online use. Hence, much work was done to optimize the exact computation, or approximate it through sampling or modeling. This paper introduces a new approximation technique PG2S that is based on reference popularity and gap distance. This approximation is exact under the Independent Reference Model (IRM). The technique is further extended, using machine learning, to PG2S+ for non-IRM reference patterns. Extensive experiments show that PG2S+ is much more accurate and robust than other state-of-the-art algorithms for determining stack distance. PG2S+ is the first technique to exploit the strong correlation among reference popularity, gap distance and stack distance.
Patient-Trial Matching with Deep Embedding and Entailment Prediction
Authors: Xingyao Zhang, Cao Xiao, Lucas Glass, Jimeng Sun
Venue: WWW
Year: 2020

Clinical trials are essential for drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. The core problem of patient-trial matching is to find qualified patients for a trial, where patient information is stored in electronic health records (EHR) while trial eligibility criteria (EC) are described in text documents available on the web. How to represent longitudinal patient EHR? How to extract complex logical rules from EC? Most existing works rely on manual rule-based extraction, which is time consuming and inflexible for complex inference. To address these challenges, we proposed a cross-modal inference learning model to jointly encode enrollment criteria (text) and patients records (tabular data) into a shared latent space for matching inference. pplies a pre-trained Bidirectional Encoder Representations from Transformers(BERT) model to encode clinical trial information into sentence embedding. And uses a hierarchical embedding model to represent patient longitudinal EHR. In addition, s augmented by a numerical information embedding and entailment module to reason over numerical information in both EC and EHR. These encoders are trained jointly to optimize patient-trial matching score. We evaluated n the trial-patient matching task with demonstrated on real world datasets. utperformed the best baseline by up to 12.4% in average F1.
A User-Centered Handoff Procedure in 5G Cellular Networks
Authors: Hu Zhao, Eyhab Al-Masri
Venue: WWW
Year: 2020

The 5G cellular network is being deployed across many countries and is becoming an integral technology for mobile communication. The ubiquity of the 5G services, however, will require the dissemination of a large number of base stations which translates into a significant number of handoff operations compared to earlier generations (e.g. 4G/LTE). Furthermore, Narrowband Internet of Things (NB-IoT) is a 3GPP specification designed for enabling the wireless communication of low powered IoT devices. However, NB-IoT does not provide complete support for handoff operations and is intended for fixed IoT devices. In this paper, we investigate critical handoff attributes that may affect the end-to-end service in NB-IoT and present a user-centered handoff optimization model. Results show that our proposed user-centered model significantly outperforms the generic uniform preferences across 96% of tested handoff operations.
Deep Adversarial Completion for Sparse Heterogeneous Information Network Embedding
Authors: Kai Zhao, Ting Bai, Bin Wu, Bai Wang, Youjie Zhang, Yuanyu Yang, Jian-Yun Nie
Venue: WWW
Year: 2020

Heterogeneous information network (HIN) contains multiple types of entities and relations. Most of existing HIN embedding methods learn the semantic information based on the heterogeneous structures between different entities, which are implicitly assumed to be complete. However, in real world, it is common that some relations are partially observed due to privacy or other reasons, resulting in a sparse network, in which the structure may be incomplete, and the ”unseen” links may also be positive due to the missing relations in data collection. To address this problem, we propose a novel and principled approach: a Multi-View Adversarial Completion Model (MV-ACM). Each relation space is characterized in a single viewpoint, enabling us to use the topological structural information in each view. Based on the multi-view architecture, an adversarial learning process is utilized to learn the reciprocity (i.e., complementary information) between different relations: In the generator, MV-ACM generates the complementary views by computing the similarity of the semantic representation of the same node in different views; while in the discriminator, MV-ACM discriminates whether the view is complementary by the topological structural similarity. Then we update the node’s semantic representation by aggregating neighborhoods information from the syncretic views. We conduct systematical experiments1 on six real-world networks from varied domains: AMiner, PPI, YouTube, Twitter, Amazon and Alibaba. Empirical results show that MV-ACM significantly outperforms the state-of-the-art approaches for both link prediction and node classification tasks.