{"title":"Beyond-Accuracy Goals, Again","authors":"M. de Rijke","doi":"10.1145/3539597.3572332","DOIUrl":"https://doi.org/10.1145/3539597.3572332","url":null,"abstract":"","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"26 1","pages":"2-3"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85117540","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}
{"title":"WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21 - 25, 2022","authors":"","doi":"10.1145/3488560","DOIUrl":"https://doi.org/10.1145/3488560","url":null,"abstract":"","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90829088","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}
{"title":"Multilingual and Multimodal Hate Speech Analysis in Twitter","authors":"Gretel Liz De la Peña Sarracén","doi":"10.1145/3437963.3441668","DOIUrl":"https://doi.org/10.1145/3437963.3441668","url":null,"abstract":"","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"15 1","pages":"1109-1110"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82413702","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}
{"title":"A Semantic Layer Querying Tool","authors":"Renato Stoffalette João","doi":"10.1145/3437963.3441710","DOIUrl":"https://doi.org/10.1145/3437963.3441710","url":null,"abstract":"","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"97 1","pages":"1101-1104"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80667286","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}
Our understanding of the web has been evolving from a large database of information to a Socio - Cognitive Space, where humans are not just using the web but participating in the web. World wide web has evolved into the largest source of information in the history, and it continues to grow without any known agenda. The web needs to be observed and studied to understand various impacts of it on the society (both positive and negative) and shape the future of the web and the society. This gave rise to the global grid of Web Observatories which focus and observe various aspects of the web. Web Observatories aim to share and collaborate various data sets, analysis tools and applications with all web observatories across the world. We plan to design and develop a Web Observatory called to observe and understand online social cognition. We propose that the social media on the web is acting as a Marketplace of Opinions where multiple users with differing interests exchange opinions. For a given trending topic on social media, we propose a model to identify the Signature of the trending topic which characterizes the discourse around the topic.
{"title":"Designing the Cogno-Web Observatory: To Characterize the Dynamics of Online Social Cognition","authors":"Raksha Pavagada Subbanarasimha","doi":"10.1145/3289600.3291600","DOIUrl":"https://doi.org/10.1145/3289600.3291600","url":null,"abstract":"Our understanding of the web has been evolving from a large database of information to a Socio - Cognitive Space, where humans are not just using the web but participating in the web. World wide web has evolved into the largest source of information in the history, and it continues to grow without any known agenda. The web needs to be observed and studied to understand various impacts of it on the society (both positive and negative) and shape the future of the web and the society. This gave rise to the global grid of Web Observatories which focus and observe various aspects of the web. Web Observatories aim to share and collaborate various data sets, analysis tools and applications with all web observatories across the world. We plan to design and develop a Web Observatory called to observe and understand online social cognition. We propose that the social media on the web is acting as a Marketplace of Opinions where multiple users with differing interests exchange opinions. For a given trending topic on social media, we propose a model to identify the Signature of the trending topic which characterizes the discourse around the topic.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"3 1","pages":"814-815"},"PeriodicalIF":0.0,"publicationDate":"2019-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74657493","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}
Interactive systems such as search engines or recommender systems are increasingly moving away from single-turn exchanges with users. Instead, series of exchanges between the user and the system are becoming mainstream, especially when users have complex needs or when the system struggles to understand the user's intent. Standard machine learning has helped us a lot in the single-turn paradigm, where we use it to predict: intent, relevance, user satisfaction, etc. When we think of search or recommendation as a series of exchanges, we need to turn to bandit algorithms to determine which action the system should take next, or to reinforcement learning to determine not just the next action but also to plan future actions and estimate their potential pay-off. The use of reinforcement learning for search and recommendations comes with a number of challenges, because of the very large action spaces, the large number of potential contexts, and noisy feedback signals characteristic for this domain. This presentation will survey some recent success stories of reinforcement learning for search, recommendation, and conversations; and will identify promising future research directions for reinforcement learning for search and recommendation.
{"title":"Reinforcement Learning to Rank","authors":"M. de Rijke","doi":"10.1145/3289600.3291605","DOIUrl":"https://doi.org/10.1145/3289600.3291605","url":null,"abstract":"Interactive systems such as search engines or recommender systems are increasingly moving away from single-turn exchanges with users. Instead, series of exchanges between the user and the system are becoming mainstream, especially when users have complex needs or when the system struggles to understand the user's intent. Standard machine learning has helped us a lot in the single-turn paradigm, where we use it to predict: intent, relevance, user satisfaction, etc. When we think of search or recommendation as a series of exchanges, we need to turn to bandit algorithms to determine which action the system should take next, or to reinforcement learning to determine not just the next action but also to plan future actions and estimate their potential pay-off. The use of reinforcement learning for search and recommendations comes with a number of challenges, because of the very large action spaces, the large number of potential contexts, and noisy feedback signals characteristic for this domain. This presentation will survey some recent success stories of reinforcement learning for search, recommendation, and conversations; and will identify promising future research directions for reinforcement learning for search and recommendation.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"12 1","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2019-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88156532","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}
This research presents a new set of techniques to deal with event mining from different text sources, a complex set of NLP tasks which aim to extract events of interest and their components including authors, targets, locations, and event categories. Our focus is on distributed text streams, such as tweets from different news agencies, in order to accurately retrieve events and its components by combining such sources in different ways using text stream mining. Therefore this research project aims to fill the gap between batch event mining, text stream mining and distributed data mining which have been used separately to address related learning tasks. We propose a multi-task and multi-stream mining approach to combine information from multiple text streams to accurately extract and categorise events under the assumptions of stream mining. Our approach also combines ontology matching to boost accuracy under imbalanced distributions. In addition, we plan to address two relatively unexplored event mining tasks: event coreference and event synthesis. Preliminary results show the appropriateness of our proposal, which is giving an increase of around 20% on macro prequential metrics for the event classification task.
{"title":"Event Mining over Distributed Text Streams","authors":"John Calvo Martinez","doi":"10.1145/3159652.3170462","DOIUrl":"https://doi.org/10.1145/3159652.3170462","url":null,"abstract":"This research presents a new set of techniques to deal with event mining from different text sources, a complex set of NLP tasks which aim to extract events of interest and their components including authors, targets, locations, and event categories. Our focus is on distributed text streams, such as tweets from different news agencies, in order to accurately retrieve events and its components by combining such sources in different ways using text stream mining. Therefore this research project aims to fill the gap between batch event mining, text stream mining and distributed data mining which have been used separately to address related learning tasks. We propose a multi-task and multi-stream mining approach to combine information from multiple text streams to accurately extract and categorise events under the assumptions of stream mining. Our approach also combines ontology matching to boost accuracy under imbalanced distributions. In addition, we plan to address two relatively unexplored event mining tasks: event coreference and event synthesis. Preliminary results show the appropriateness of our proposal, which is giving an increase of around 20% on macro prequential metrics for the event classification task.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"31 1","pages":"745-746"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75388135","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}
This project is focused on the design of probabilistic models for recommender systems and collaborative ltering by extending and creating new models to include rich contextual and content information (content, user social network, location, time, user intent, etc), and developing scalable approximate inference algorithms for these models. The working hypothesis is that big data analytics combined with probabilistic modelling, through automatically mining of various data sources and combining di erent latent factors explaining the user interaction with the items, can be used to better infer the user behaviour and generate improved recommendations. Fundamentally we are interested in the following questions: 1) Does additional contextual information improve the quality of recommender systems? 2) What factors (features, model, methods) are relevant in the design of personalized systems? 3) What is the relation between the social network structure, the user model and the information need of the user? How does the social context interferes with user preferences? How the evolution of the social network structure can explain changes in the user preference model? 4) Does the choice of approximate inference method have a signi cant impact on the quality of the system (quality- efficiency trade-offs)? To address some of this questions we started by proposing a model (Figure 1) based on Poisson factorization models [2], combining a social factorization model [1] and a topic based factorization [3]. The main idea is to combine content latent factor (topic, tags, etc) and trust between users (trust weight in a social graph) in a way that both sources of information have additive e ects in the observed ratings. In the case of Poisson models, this additive constraint will induce non-negative latent factors to be more sparse and avoid overfitting (in comparison the Gausian based models [2]. The main objective at this point is to compare models that incorporated both source of information (content and social networks). The next steps will include empirical validation. Concluding, we are interested in the interplay between large scale data mining and probabilistic modeling in the design of recommender systems. One initial approach we are pursuing is to model content and social network feature in a Poisson latent variable model. Our main objective in the future is the development of methods with competitive computational complexity to perform inference using het- erogeneous data in dynamical probabilistic models, as well as exploring the scalability limits of the models we propose.
{"title":"New Probabilistic Models for Recommender Systems with Rich Contextual and Content Information","authors":"Eliezer de Souza da Silva","doi":"10.1145/3018661.3022751","DOIUrl":"https://doi.org/10.1145/3018661.3022751","url":null,"abstract":"This project is focused on the design of probabilistic models for recommender systems and collaborative ltering by extending and creating new models to include rich contextual and content information (content, user social network, location, time, user intent, etc), and developing scalable approximate inference algorithms for these models. The working hypothesis is that big data analytics combined with probabilistic modelling, through automatically mining of various data sources and combining di erent latent factors explaining the user interaction with the items, can be used to better infer the user behaviour and generate improved recommendations. Fundamentally we are interested in the following questions: 1) Does additional contextual information improve the quality of recommender systems? 2) What factors (features, model, methods) are relevant in the design of personalized systems? 3) What is the relation between the social network structure, the user model and the information need of the user? How does the social context interferes with user preferences? How the evolution of the social network structure can explain changes in the user preference model? 4) Does the choice of approximate inference method have a signi cant impact on the quality of the system (quality- efficiency trade-offs)? To address some of this questions we started by proposing a model (Figure 1) based on Poisson factorization models [2], combining a social factorization model [1] and a topic based factorization [3]. The main idea is to combine content latent factor (topic, tags, etc) and trust between users (trust weight in a social graph) in a way that both sources of information have additive e ects in the observed ratings. In the case of Poisson models, this additive constraint will induce non-negative latent factors to be more sparse and avoid overfitting (in comparison the Gausian based models [2]. The main objective at this point is to compare models that incorporated both source of information (content and social networks). The next steps will include empirical validation. Concluding, we are interested in the interplay between large scale data mining and probabilistic modeling in the design of recommender systems. One initial approach we are pursuing is to model content and social network feature in a Poisson latent variable model. Our main objective in the future is the development of methods with competitive computational complexity to perform inference using het- erogeneous data in dynamical probabilistic models, as well as exploring the scalability limits of the models we propose.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"53 1","pages":"839"},"PeriodicalIF":0.0,"publicationDate":"2017-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76903583","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}
In this article, I present the questions that I seek to answer in my PhD research. I posit to analyze natural language text with the help of semantic annotations and mine important events for navigating large text corpora. Semantic annotations such as named entities, geographic locations, and temporal expressions can help us mine events from the given corpora. These events thus provide us with useful means to discover the locked knowledge in them. I pose three problems that can help unlock this knowledge vault in semantically annotated text corpora: i. identifying important events; ii. semantic search; iii. and event analytics.
{"title":"Event Search and Analytics: Detecting Events in Semantically Annotated Corpora for Search & Analytics","authors":"Dhruv Gupta","doi":"10.1145/2835776.2855083","DOIUrl":"https://doi.org/10.1145/2835776.2855083","url":null,"abstract":"In this article, I present the questions that I seek to answer in my PhD research. I posit to analyze natural language text with the help of semantic annotations and mine important events for navigating large text corpora. Semantic annotations such as named entities, geographic locations, and temporal expressions can help us mine events from the given corpora. These events thus provide us with useful means to discover the locked knowledge in them. I pose three problems that can help unlock this knowledge vault in semantically annotated text corpora: i. identifying important events; ii. semantic search; iii. and event analytics.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78611936","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：