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The 3rd international Workshop on Machine Learning on Graphs (MLoG) 23

The 3rd International Workshop on Machine Learning on Graphs...

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16th international conference on web search and data mining

作者： Derr, Tyler Ma, Yao Rozemberczki, Benedek Shah, Neil Pan, Shirui Vanderbilt Univ Nashville TN 37235 USA New Jersey Inst Technol Newark NJ USA Isomorph Labs London England Snap Inc Santa Monica CA USA Griffith Univ Nathan Qld Australia

ISBN: (纸本)9781450394079

Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today's world, we are generating and gathering data in a much faster and more diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way. Thus, we organize The 3rd international Workshop on Machine Learning on Graphs (MLoG)(1), held in conjunction with the 16th acm conference on web search and data mining (wsdm), which provides a venue to gather academia researchers and industry researchers/practitioners to present the recent progress on machine learning on graphs.

关键词： Machine Learning on Graphs

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The 3^rd internationalWorkshop on Interactive and Scalable Information Retrieval Methods for eCommerce (ISIR-eCom 2024) 24

The 3<SUP>rd</SUP> InternationalWorkshop on Interactive and ...

引用

17th acm international conference on web search and data mining (wsdm)

作者： Dave, Vachik S. Pang, Linsey Cui, Xiquan Luo, Chen Zamani, Hamed Wu, Lingfei Karypis, George Walmart Global Tech Sunnyvale CA 94086 USA Salesforce San Francisco CA USA Home Depot Atlanta GA USA Amazon Seattle WA USA Univ Massachusetts Amherst Amherst MA USA Pinterest New York NY USA Univ Minnesota Twin Cities Minneapolis MN USA

ISBN: (纸本)9798400703713

Over the past few years, consumer behavior has shifted from traditional in-store shopping to online shopping. For example, eCommerce sales have grown from around 5% of total US sales in 2012 to around 15.4% in year 2023. This rapid growth of eCommerce has created new challenges and vital new requirements for intelligent information retrieval systems. Which lead to the primary motivations of this workshop:(1) Since the pandemic hit, eCommerce became an important part of people's routine and they started using online shop- ping for smallest grocery items to big electronics as well as cars. With such a large assortment of products and millions of users, achieving higher scalability without losing accuracy is a leading concern for information retrieval systems for eCommerce.(2) The diverse buyers make the relevance of the results highly subjective, because relevance varies for different buyers. The most suitable and intuitive solution to this problem is to make the system interactive and provide correct relevance for different users. Hence, interactive information retrieval systems are becoming necessity in eCommerce.(3) To handle sudden change in buyers' behavior, industries adopted existing sub-optimal information retrieval techniques for various eCommerce tasks. Parallelly, they also started exploring/researching for better solutions and in dire need of help from research *** workshop will provide a forum to discuss and learn the latest trends for interactive and scalable information retrieval approaches for eCommerce. It will provide academic and industrial researchers a platform to present their latest works, share research ideas, present and discuss various challenges, and identify the areas where further research is needed. It will foster the development of a strong research community focused on solving eCommerce-related information retrieval problems that provide superior eCommerce experience to all users.

关键词： Information Retrieval Interactive systems Large Language models (LLMs) in eCommerce Recommender Systems eCommerce search Ranking models Natural Language Processing (NLP) for eCommerce

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Vigilance Against Autonomous mining of Trade Secrets During Python-Based web Crawling of data 3

Vigilance Against Autonomous Mining of Trade Secrets During ...

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2023 3rd international conference on Big data, Artificial Intelligence and Risk Management, ICBAR 2023

作者： Xu, Jikuan Qingdao China

ISBN: (纸本)9798400716478

With the rapid development of the Internet, data has become an important asset, in order to further simplify the search and accurate positioning of network resources, web crawler technology is emerging, Python with its high degree of compatibility and a large community, has become the mainstream technology of web crawler. However, in recent years, the number of infringement cases has increased gradually, and the intentional or unintentional mining of commercial secrets in the process of web crawling has become a major problem that cannot be avoided. In this paper, based on the technical aspects of Python web crawling data process, we take Scrapy architecture as an example to explore how to effectively circumvent the problem of illegally crawling enterprise trade secrets. The study shows that when dealing with anti-crawling technical measures, we should be alert to the object protected by anti-crawling technology, respect the rules and agreements of the website, respect the commercial secrets and legitimate rights and interests of the enterprise, and must not avoid this technology to obtain data without exception. The reasonable use of web crawlers is of great significance in promoting the flow of data and the development of enterprises as well as the social economy. © 2023 acm.

关键词： Python

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Semantic Matching in APP search 15

Semantic Matching in APP Search

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8th acm international conference on web search and data mining (wsdm)

作者： Zhuo, Juchao Huang, Zeqian Liu, Yunfeng Kang, Zhanhui Cao, Xun Li, Mingzhi Jin, Long Tencent Inc Netac BldgHigh Tech Sixth South Rd Shenzhen 518000 Peoples R China

ISBN: (纸本)9781450333177

Past years, with the growth of smart-phones and applications, APP market has become an important mobile internet portal. As an important function in application market, APP search gains lots of ***, mismatch between queries and APP is the most critical problem in APP search because of less text within term matching search engine. In this talk, we describe a semantic matching architecture in APP search--which mining topics and tags in big data. It enriches query and APP representations with topics and tags to achieve semantic matching in search. Some challenge must be considered: 1) How to extract tag-APP relationship from large web text. 2) How to use machine learning technologies to process de-noising and computing confidence. 3) How to hybrid ranking apps retrieved by different matching method. These will be introduced in some of our related works and as examples to describe how semantic matching is used in Tencent MyApp, an application market which serving hundreds of millions of users.

关键词： Semantic matching app search app store topic mode tag mining

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wsdm 2010 - Proceedings of the 3rd acm international conference on web search and data mining

WSDM 2010 - Proceedings of the 3rd ACM International Confere...

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3rd acm international conference on web search and data mining, wsdm 2010

ISBN: (纸本)9781605588896

The proceedings contain 45 papers. The topics discussed include: leveraging temporal dynamics of document content in relevance ranking;towards recency ranking in web search;learning concept importance using a weighted dependence model;query reformulation using anchor text;tagging human knowledge;precomputing search features for fast and accurate query classification;pairwise interaction tensor factorization for personalized tag recommendation;coupled semi-supervised learning for information extraction;data-oriented content query system: searching for data into text on the web;corroborating information from disagreeing views;ranking with query-dependent loss for web search;an optimization framework for query recommendation;improving quality of training data for learning to rank using click-through data;a model to estimate intrinsic document relevance from the clickthrough logs of a web search engine;large scale query log analysis of re-finding;and inferring search behaviors using partially observable Markov (POM) model.

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3rd international conference on web Intelligence, mining and Semantics, WIMS 2013

3rd International Conference on Web Intelligence, Mining and...

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3rd international conference on web Intelligence, mining and Semantics, WIMS 2013

ISBN: (纸本)9781450318501

The proceedings contain 45 papers. The topics discussed include: physical cyber social computing for human experience;contextual synchronization for social collaboration;guidelines for multilingual linked data;discovery hub: a discovery engine on the top of DBpedia;data processing and semantics for advanced Internet of things (IoT) applications: modeling, annotation, integration, and perception;towards topics-based, semantics-assisted news search;iridescent: a tool for rapid semantic annotation of web service descriptions;web object identification for web automation and meta-search;utility-driven evolution recommender for a constrained ontology;construction and applicability of military ontology for semantic data processing;crafting a balance between big data utility and protection in the semantic data cloud;and automatic classification of documents in cold-start scenarios.

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Context ontologies for recommending from the social web 13

Context ontologies for recommending from the social web

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3rd Workshop on Context-Awareness in Retrieval and Recommendation, CaRR 2013 - In Conjunction with the 6th acm international conference on web search and data mining, wsdm 2013

作者： Hurrell, Eoin Smeaton, Alan F. CLARITY: Centre for Sensor Web Technologies School of Computing Dublin University Glasnevin Dublin 9 Ireland

ISBN: (纸本)9781450318471

Investigations into combining context and recommendation has resulted in much fruitful research which has improved recommender systems. Such contextual information has come in many forms and been used in different ways, successfully offering better in-situ suggestions. Factors such as location, time of recommendation, etc. have proven themselves as useful contributors to exploiting context. One issue, however, is the importance placed on each aspect of context, especially as new forms of recommendation are developed. Context is traditionally incorporated into recommenders at design-time, as a filter or as an integral part of how users are modelled, but the importance placed on each aspect is not often examined. Social recommenders and systems that draw on the wealth of data present in social networks frequently have access to far more contextual factors than traditional recommenders, making user relationships to these factors all the more important. The main contribution of this paper is to provide an examination of contextual priorities from the social web, which prove useful to recommender research in the area. This ontological examination of context shows that users have different priorities when it comes to context with a large variation in the suitability of each contextual factor in predicting good recommendations. In addition, this paper presents and discusses an approach to individually tailoring context ontologies (allowing for dynamically generated context sets), evaluating contextual factors in recommending from the social web. Copyright 2013 acm.

关键词： Recommender systems

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Discovery Hub: A discovery engine on the top of DBpedia

Discovery Hub: A discovery engine on the top of DBpedia

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3rd international conference on web Intelligence, mining and Semantics, WIMS 2013

作者： Marie, Nicolas Gandon, Fabien Legrand, Damien Ribiere, Myriam Alcatel-Lucent Bell Labs INRIA Sophia-Antipolis France INRIA Sophia-Antipolis 2004 route des Lucioles 06902 Sophia Antipolis France Alcatel-Lucent Bell Labs Route de Villejust 91620 Nozay France

ISBN: (纸本)9781450318501

This paper supports the Discovery Hub demonstration proposal. web growth, both in size and diversity, and users' growing expectations increase the need for innovative search approaches and technologies. Exploratory search systems are built specifically to help user in cognitive consuming search tasks like learning or investigation. Some of these systems are built on the top of linked data and use its semantic richness to provide cognitivelyoptimized search experiences. This paper presents the Discovery Hub operational prototype after detailing its Semantic Spreading Activation (SSA) algorithm. This latter processes linked data in on-the-fly and does not require partial or total results preprocessing. This on-the-fly processing offers advantages when addressing evolving linked data datasets and querying flexibility. Copyright © 2013 acm.

关键词： Linked data

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MOWS : Macro and micro online webview selection

MOWS : Macro and micro online webview selection

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3rd international conference on web Intelligence, mining and Semantics, WIMS 2013

作者： Ammar, Ali Ben Abdellatif, Abdelaziz Ghezala, Henda Ben Institut Supérieur D'Informatique Et de Gestion Kairouan 3100 Tunisia Faculté des Sciences de Tunis Tunis 2092 Tunisia Ecole Nationale des Sciences de l'Informatique Manouba 2010 Tunisia

ISBN: (纸本)9781450318501

In this paper we present an approach, called MOWS, to select materialized webview in data-intensive websites (DIWS). A webview is a static instance of a dynamic web page. The materialization of webviews consists of storing the results of some requests on the server in order to avoid repetitive data generation from the sources. The aim is to improve the query response time. Our contribution in this work is to apply two steps for the selection of webviews. The first one is executed periodically and it consists of filtering the candidate webviews. It is called macroselection. The second is executed online and it consists of selecting the materialized webviews. It is called micro-selection. Our experiment results show that our solution is very efficient to improve query response time especially for the websites with high size. For this type of websites, our approach overcomes the existing solutions and it improves their query response times by more than 70%. Copyright © 2013 acm.

关键词： Quality of service

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"I'm eating a sandwich in Glasgow": Modeling locations with tweets

"I'm eating a sandwich in Glasgow": Modeling locations with ...

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3rd international Workshop on search and mining User-Generated Contents, SMUC'11, Held in Conjunction with the 20th acm conference on Information and Knowledge Management, CIKM 2011

作者： Kinsella, Sheila Murdock, Vanessa O'Hare, Neil Digital Enterprise Research Institute National University of Ireland Galway Ireland Yahoo Research Barcelona Spain CLARITY: Centre for Sensor Web Technologies Dublin City University Ireland

ISBN: (纸本)9781450309493

Social media such as Twitter generate large quantities of data about what a person is thinking and doing in a particular location. We leverage this data to build models of locations to improve our understanding of a user's geographic context. Understanding the user's geographic context can in turn enable a variety of services that allow us to present information, recommend businesses and services, and place advertisements that are relevant at a hyper-local level. In this paper we create language models of locations using coordinates extracted from geotagged Twitter data. We model locations at varying levels of granularity, from the zip code to the country level. We measure the accuracy of these models by the degree to which we can predict the location of an individual tweet, and further by the accuracy with which we can predict the location of a user. We find that we can meet the performance of the industry standard tool for predicting both the tweet and the user at the country, state and city levels, and far exceed its performance at the hyper-local level, achieving a three- to ten-fold increase in accuracy at the zip code level. © 2011 acm.

关键词： Location

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