Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining最新文献_第7页

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Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3330867

Daheng Wang, Tianwen Jiang, N. Chawla, Meng Jiang

引用次数: 3

Chainer: A Deep Learning Framework for Accelerating the Research Cycle Chainer:加速研究周期的深度学习框架

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3330756

Seiya Tokui, Ryosuke Okuta, Takuya Akiba, Yusuke Niitani, Toru Ogawa, S. Saito, Shuji Suzuki, Kota Uenishi, Brian K. Vogel, Hiroyuki Yamazaki Vincent

引用次数: 111

Real-time Event Detection on Social Data Streams 基于社交数据流的实时事件检测

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3330689

Mateusz Fedoryszak, Brent Frederick, V. Rajaram, Changtao Zhong

引用次数: 72

Carousel Ads Optimization in Yahoo Gemini Native Carousel广告优化在雅虎双子座原生

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3330740

M. Aharon, O. Somekh, Avi Shahar, Assaf Singer, Baruch Trayvas, Hadas Vogel, Dobrislav Dobrev

{"title":"Carousel Ads Optimization in Yahoo Gemini Native","authors":"M. Aharon, O. Somekh, Avi Shahar, Assaf Singer, Baruch Trayvas, Hadas Vogel, Dobrislav Dobrev","doi":"10.1145/3292500.3330740","DOIUrl":"https://doi.org/10.1145/3292500.3330740","url":null,"abstract":"Yahoo's native advertising (also known as Gemini native) serves billions of ad impressions daily, reaching a yearly run-rate of many hundred of millions USD. Driving Gemini native models for predicting both click probability (pCTR) and conversion probability (pCONV) is OFFSET - a feature enhanced collaborative-filtering (CF) based event prediction algorithm. The predicted pCTRs are then used in Gemini native auctions to determine which ads to present for each serving event. A fast growing segment of Gemini native is Carousel ads that include several cards (or assets) which are used to populate several slots within the ad. Since Carousel ad slots are not symmetrical and some are more conspicuous than others, it is beneficial to render assets to slots in a way that maximizes revenue. In this work we present a post-auction successive elimination based approach for ranking assets according to their click trough rate (CTR) and render the carousel accordingly, placing higher CTR assets in more conspicuous slots. After a successful online bucket showing 8.6% CTR and 4.3% CPM (or revenue) lifts over a control bucket that uses predefined advertisers assets-to-slots mapping, the carousel asset optimization (CAO) system was pushed to production and is serving all Gemini native traffic since. A few months after CAO deployment, we have already measured an almost 40% increase in carousel ads revenue. Moreover, the entire revenue growth is related to CAO traffic increase due to additional advertiser demand, which demonstrates a high advertisers' satisfaction of the product.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132222660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Active Deep Learning for Activity Recognition with Context Aware Annotator Selection 基于上下文感知注释器选择的活动识别主动深度学习

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3330688

H. S. Hossain, Nirmalya Roy

{"title":"Active Deep Learning for Activity Recognition with Context Aware Annotator Selection","authors":"H. S. Hossain, Nirmalya Roy","doi":"10.1145/3292500.3330688","DOIUrl":"https://doi.org/10.1145/3292500.3330688","url":null,"abstract":"Machine learning models are bounded by the credibility of ground truth data used for both training and testing. Regardless of the problem domain, this ground truth annotation is objectively manual and tedious as it needs considerable amount of human intervention. With the advent of Active Learning with multiple annotators, the burden can be somewhat mitigated by actively acquiring labels of most informative data instances. However, multiple annotators with varying degrees of expertise poses new set of challenges in terms of quality of the label received and availability of the annotator. Due to limited amount of ground truth information addressing the variabilities of Activity of Daily Living (ADLs), activity recognition models using wearable and mobile devices are still not robust enough for real-world deployment. In this paper, we first propose an active learning combined deep model which updates its network parameters based on the optimization of a joint loss function. We then propose a novel annotator selection model by exploiting the relationships among the users while considering their heterogeneity with respect to their expertise, physical and spatial context. Our proposed model leverages model-free deep reinforcement learning in a partially observable environment setting to capture the action-reward interaction among multiple annotators. Our experiments in real-world settings exhibit that our active deep model converges to optimal accuracy with fewer labeled instances and achieves ~8% improvement in accuracy in fewer iterations.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133040318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 31

Earth Observations from a New Generation of Geostationary Satellites 新一代地球同步卫星的地球观测

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3340413

R. Nemani

引用次数: 0

Sequential Anomaly Detection using Inverse Reinforcement Learning 基于逆强化学习的序列异常检测

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3330932

Min-hwan Oh, G. Iyengar

{"title":"Sequential Anomaly Detection using Inverse Reinforcement Learning","authors":"Min-hwan Oh, G. Iyengar","doi":"10.1145/3292500.3330932","DOIUrl":"https://doi.org/10.1145/3292500.3330932","url":null,"abstract":"One of the most interesting application scenarios in anomaly detection is when sequential data are targeted. For example, in a safety-critical environment, it is crucial to have an automatic detection system to screen the streaming data gathered by monitoring sensors and to report abnormal observations if detected in real-time. Oftentimes, stakes are much higher when these potential anomalies are intentional or goal-oriented. We propose an end-to-end framework for sequential anomaly detection using inverse reinforcement learning (IRL), whose objective is to determine the decision-making agent's underlying function which triggers his/her behavior. The proposed method takes the sequence of actions of a target agent (and possibly other meta information) as input. The agent's normal behavior is then understood by the reward function which is inferred via IRL. We use a neural network to represent a reward function. Using a learned reward function, we evaluate whether a new observation from the target agent follows a normal pattern. In order to construct a reliable anomaly detection method and take into consideration the confidence of the predicted anomaly score, we adopt a Bayesian approach for IRL. The empirical study on publicly available real-world data shows that our proposed method is effective in identifying anomalies.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122070924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 59

Optuna: A Next-generation Hyperparameter Optimization Framework Optuna:下一代超参数优化框架

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3330701

Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, Masanori Koyama

{"title":"Optuna: A Next-generation Hyperparameter Optimization Framework","authors":"Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, Masanori Koyama","doi":"10.1145/3292500.3330701","DOIUrl":"https://doi.org/10.1145/3292500.3330701","url":null,"abstract":"The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that allows users to construct the parameter search space dynamically, (2) efficient implementation of both searching and pruning strategies, and (3) easy-to-setup, versatile architecture that can be deployed for various purposes, ranging from scalable distributed computing to light-weight experiment conducted via interactive interface. In order to prove our point, we will introduce Optuna, an optimization software which is a culmination of our effort in the development of a next generation optimization software. As an optimization software designed with define-by-run principle, Optuna is particularly the first of its kind. We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications. Our software is available under the MIT license (https://github.com/pfnet/optuna/).","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114933029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2524

PinText: A Multitask Text Embedding System in Pinterest PinText:一个多任务文本嵌入系统在Pinterest

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3330671

Jinfeng Zhuang, Yu Liu

{"title":"PinText: A Multitask Text Embedding System in Pinterest","authors":"Jinfeng Zhuang, Yu Liu","doi":"10.1145/3292500.3330671","DOIUrl":"https://doi.org/10.1145/3292500.3330671","url":null,"abstract":"Text embedding is a fundamental component for extracting text features in production-level data mining and machine learning systems given textual information is the most ubiqutious signals. However, practitioners often face the tradeoff between effectiveness of underlying embedding algorithms and cost of training and maintaining various embedding results in large-scale applications. In this paper, we propose a multitask text embedding solution called PinText for three major vertical surfaces including homefeed, related pins, and search in Pinterest, which consolidates existing text embedding algorithms into a single solution and produces state-of-the-art performance. Specifically, we learn word level semantic vectors by enforcing that the similarity between positive engagement pairs is larger than the similarity between a randomly sampled background pairs. Based on the learned semantic vectors, we derive embedding vector of a user, a pin, or a search query by simply averaging its word level vectors. In this common compact vector space, we are able to do unified nearest neighbor search with hashing by Hadoop jobs or dockerized images on Kubernetes cluster. Both offline evaluation and online experiments show effectiveness of this PinText system and save storage cost of multiple open-sourced embeddings significantly.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"665 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116100463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

AKUPM

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3330705

Xiaoli Tang, Tengyun Wang, Haizhi Yang, Hengjie Song

{"title":"AKUPM","authors":"Xiaoli Tang, Tengyun Wang, Haizhi Yang, Hengjie Song","doi":"10.1145/3292500.3330705","DOIUrl":"https://doi.org/10.1145/3292500.3330705","url":null,"abstract":"Recently, much attention has been paid to the usage of knowledge graph within the context of recommender systems to alleviate the data sparsity and cold-start problems. However, when incorporating entities from a knowledge graph to represent users, most existing works are unaware of the relationships between these entities and users. As a result, the recommendation results may suffer a lot from some unrelated entities. In this paper, we investigate how to explore these relationships which are essentially determined by the interactions among entities. Firstly, we categorize the interactions among entities into two types: inter-entity-interaction and intra-entity-interaction. Inter-entity-interaction is the interactions among entities that affect their importances to represent users. And intra-entity-interaction is the interactions within an entity that describe the different characteristics of this entity when involved in different relations. Then, considering these two types of interactions, we propose a novel model named Attention-enhanced Knowledge-aware User Preference Model (AKUPM) for click-through rate (CTR) prediction. More specifically, a self-attention network is utilized to capture the inter-entity-interaction by learning appropriate importance of each entity w.r.t the user. Moreover, the intra-entity-interaction is modeled by projecting each entity into its connected relation spaces to obtain the suitable characteristics. By doing so, AKUPM is able to figure out the most related part of incorporated entities (i.e., filter out the unrelated entities). Extensive experiments on two real-world public datasets demonstrate that AKUPM achieves substantial gains in terms of common evaluation metrics (e.g., AUC, ACC and Recall@top-K) over several state-of-the-art baselines.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115580271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4