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检索条件"任意字段=5th ACM International Conference on Web Search and Data Mining, WSDM 2012"

共 80 条记录，以下是51-60 订阅

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Beyond Hard Negatives in Product search: Semantic Matching using One-Class Classification (SMOCC) 23

Beyond Hard Negatives in Product Search: Semantic Matching u...

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16th international conference on web search and data mining

作者： Bhattacharya, Arindam Gandhi, Ankit Huddar, Vijay Ankith, M. S. Moroney, Aayush Saroop, Atul Bhagat, Rahul Amazon Bangalore Karnataka India Amazon Seattle WA USA

ISBN: (纸本)9781450394079

Semantic matching is an important component of a product search pipeline. Its goal is to capture the semantic intent of the search query as opposed to the syntactic matching performed by a lexical matching system. A semantic matching model captures relationships like synonyms, and also captures common behavioral patterns to retrieve relevant results by generalizing from purchase data. Semantic matching models however suffer from lack of availability of informative negative examples for model training. Various methods have been proposed in the past to address this issue based upon hard-negative mining and contrastive learning. In this work, we propose a novel method for semantic matching based on one-class classification called SMOCC. Given a query and a relevant product, SMOCC generates the representation of an informative negative which is then used to train the model. Our method is based on the idea of generating negatives by using adversarial search in the neighborhood of the positive examples. We also propose a novel approach for selecting the radius to generate adversarial negative products around queries based on the model's understanding of the query. Depending on how we select the radius, we propose two variants of our method: SMOCC-QS, that quantizes the queries using their specificity, and SMOCC-EM, that uses expectation-maximization paradigm to iteratively learn the best radius. We show that our method outperforms the state-of-the-art hard negative mining approaches by increasing the purchase recall by 3 percentage points, and improving the percentage of exacts retrieved by up to 5 percentage points while reducing irrelevant results by 1.8 percentage points.

关键词： semantic search one class classification

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Comparative web search Questions 20

Comparative Web Search Questions

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13th Annual acm international conference on web search and data mining (wsdm)

作者： Bondarenko, Alexander Braslayski, Pavel Voelske, Michael Aly, Rami Froebe, Maik Panchenko, Alexander Biemann, Chris Stein, Benno Hagen, Matthias Martin Luther Univ Halle Wittenberg Halle Germany Ural Fed Univ Ekaterinburg Russia Natl Res Univ Higher Sch Econ Moscow Russia JetBrains Res Moscow Russia Bauhaus Univ Weimar Weimar Germany Univ Cambridge Cambridge England Skolkovo Inst Sci & Technol Moscow Russia Univ Hamburg Hamburg Germany

ISBN: (纸本)9781450368223

We analyze comparative questions, i.e., questions asking to compare different items, that were submitted to Yandex in 2012. Responses to such questions might be quite different from the simple "ten blue links" and could, for example, aggregate pros and cons of the different options as direct answers. However, changing the result presentation is an intricate decision such that the classification of comparative questions forms a highly precision-oriented task. From a year-long Yandex log, we annotate a random sample of 50,000 questions;2.8% of which are comparative. For these annotated questions, we develop a precision-oriented classifier by combining carefully hand-crafted lexico-syntactic rules with feature based and neural approaches achieving a recall of 0.6 at a perfect precision of 1.0. After running the classifier on the full year log (on average, there is at least one comparative question per second), we analyze 6,250 comparative questions using more fine-grained subclasses (e.g., should the answer be a "simple" fact or rather a more verbose argument) for which individual classifiers are trained. An important insight is that more than 65% of the comparative questions demand argumentation and opinions, i.e., reliable direct answers to comparative questions require more than the facts from a search engine's knowledge graph. In addition, we present a qualitative analysis of the underlying comparative information needs (separated into 14 categories like consumer electronics or health), their seasonal dynamics, and possible answers from community question answering platforms.

关键词： Question answering Question classification Query log analysis

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Jointly Optimized Neural Coreference Resolution with Mutual Attention 20

Jointly Optimized Neural Coreference Resolution with Mutual ...

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13th Annual acm international conference on web search and data mining (wsdm)

作者： Ma, Jie Liu, Jun Li, Yufei Hu, Xin Pan, Yudai Sun, Shen Lin, Qika Xi An Jiao Tong Univ Natl Engn Lab Big Data Analyt Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ R&D Shaanxi Prov Key Lab Satellite & Terr Network Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781450368223

Coreference resolution aims at recognizing different forms in a document which refer to the same entity in the real world. Although many models have been proposed and achieved success, there still exist some challenges. Recent models that use recurrent neural networks to obtain mention representations ignore dependencies between spans and their proceeding distant spans, which will lead to predicted clusters that are locally consistent but globally inconsistent. In addition, these models are trained only by maximizing the marginal likelihood of gold antecedent spans from coreference clusters, which will make some gold mentions undetectable and cause unsatisfactory coreference results. To address these challenges, we propose a neural coreference resolution model. It employs mutual attention to take into account the dependencies between spans and their proceeding spans directly (use attention mechanism to capture global information between spans and their proceeding spans). And our model is trained by jointly optimizing mention clustering and imbalanced mention detection, which enables it to detect more gold mentions in a document to make more accurate coreference decisions. Experimental results on the CoNLL-2012 English dataset show that our model can detect the most gold mentions and achieve the state-of-the-art coreference performance compared with baselines.

关键词： neural coreference resolution entity recognition natural language processing mutual attention joint optimization

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Personalized click model through collaborative filtering 12

Personalized click model through collaborative filtering

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5th acm international conference on web search and data mining, wsdm 2012

作者： Shen, Si Hu, Botao Chen, Weizhu Yang, Qiang Hong Kong University of Science and Technology Hong Kong Institute for Interdisciplinary Information Sciences Tsinghua University China Microsoft Research Asia China

ISBN: (纸本)9781450307475

Click modeling aims to interpret the users' search click data in order to predict their clicking behavior. Existing models can well characterize the position bias of documents and snippets in relation to users' mainstream click behavior. Yet, current advances depict users' search actions only in a general setting by implicitly assuming that all users act in the same way, regardless of the fact that anyone, motivated with some individual interest, is more likely to click on a link than others. It is in light of this that we put forward a novel personalized click model to describe the user-oriented click preferences, which applies and extends matrix / tensor factorization from the view of collaborative filtering to connect users, queries and documents together. Our model serves as a generalized personalization framework that can be incorporated to the previously proposed click models and, in many cases, to their future extensions. Despite the sparsity of search click data, our personalized model demonstrates its advantage over the best click models previously discussed in the web-search literature, supported by our large-scale experiments on a real dataset. A delightful bonus is the model's ability to gain insights into queries and documents through latent feature vectors, and hence to handle rare and even new query-document pairs much better than previous click models. Copyright 2012 acm.

关键词： search engines

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Embedding of Embedding (EOE) : Joint Embedding for Coupled Heterogeneous Networks 17

Embedding of Embedding (EOE) : Joint Embedding for Coupled H...

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10th acm international conference on web search and data mining (wsdm)

作者： Xu, Linchuan Wei, Xiaokai Cao, Jiannong Yu, Philip S. Hong Kong Polytech Univ Hong Kong Peoples R China Univ Illinois Chicago IL USA

ISBN: (纸本)9781450346757

Network embedding is increasingly employed to assist network analysis as it is effective to learn latent features that encode linkage information. Various network embedding methods have been proposed, but they are only designed for a single network scenario. In the era of big data, different types of related information can be fused together to form a coupled heterogeneous network, which consists of two different but related sub-networks connected by inter-network edges. In this scenario, the inter-network edges can act as complementary information in the presence of intra-network ones. this complementary information is important because it can make latent features more comprehensive and accurate. And it is more important when the intra-network edges are absent, which can be referred to as the cold-start problem. In this paper, we thus propose a method named embedding of embedding (EOE) for coupled heterogeneous networks. In the EOE, latent features encode not only intra-network edges, but also inter-network ones. To tackle the challenge of heterogeneities of two networks, the EOE incorporates a harmonious embedding matrix to further embed the embeddings that only encode intra-network edges. Empirical experiments on a variety of real-world datasets demonstrate the EOE outperforms consistently single network embedding methods in applications including visualization, link prediction multi-class classification, and multi-label classification.

关键词： Network Embedding Coupled Heterogeneous Networks data mining

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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 ...

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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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Proceedings of the Workshop on Interactive data mining, WIDM 2019

Proceedings of the Workshop on Interactive Data Mining, WIDM...

引用

1st Workshop on Interactive data mining, WIDM 2019, co-located with 12th acm international conference on web search and data mining, wsdm 2019

ISBN: (纸本)9781450362962

the proceedings contain 5 papers. the topics discussed include: a system for proactive management of raw milk quality;interactive clustering for exploring multiple data streams at different time scales and granularity;incremental causal discovery and visualization;interactive feature extraction for diagnostic trouble codes in predictive maintenance a case study from automotive domain;and interactive-COSMO: consensus self-organized models for fault detection with expert feedback.

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Your Cart tells You: Inferring Demographic Attributes from Purchase data 16

Your Cart tells You: Inferring Demographic Attributes from P...

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9th Annual acm international conference on web search and data mining (wsdm)

作者： Wang, Pengfei Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci & Technol Beijing 100864 Peoples R China

ISBN: (纸本)9781450337168

Demographic attributes play an important role in retail market to characterize different types of users. Such signals however are often only available for a small fraction of users in practice due to the difficulty in manual collection process by retailers. In this paper, we aim to harness the power of big data to automatically infer users' demographic attributes based on their purchase data. Typically, demographic prediction can be formalized as a multi-task multi-class prediction problem, i.e., multiple demographic attributes (e.g., gender, age and income) are to be inferred for each user where each attribute may belong to one of N possible classes (N>2). Most previous work on this problem explores different types of features and usually predicts different attributes independently. However, modeling the tasks separately may lose the ability to leverage the correlations among different attributes. Meanwhile, manually defined features require professional knowledge and often suffer from under specification. To address these problems, we propose a novel Structured Neural Embedding (SNE) model to automatically learn the representations from users' purchase data for predicting multiple demographic attributes simultaneously. Experiments are conducted on a real-world retail dataset where five attributes (gender, marital status, income, age, and education level) are to be predicted. the empirical results show that our SNE model can improve the performance significantly compared with state-of-the-art baselines.

关键词： Algorithm theory Experimentation Performance Demographic attribute Structured Neural Embedding multi-task multi-class prediction

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Extraction of Product Specifications from the web - Going Beyond Tables and Lists 2022

Extraction of Product Specifications from the Web - Going Be...

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5th acm India Joint international conference on data Science and Management of data (CoDS-COMAD) / 9th acm IKDD CODS conference / 26th COMAD conference

作者： Gangadhar, Govind Krishnan Kulkarni, Ashish Clustr Bangalore Karnataka India

ISBN: (纸本)9781450385824

E-commerce product pages on the web often present product specification data in structured tabular blocks. Extraction of these product attribute-value specifications has benefited applications like product catalogue curation, search, question answering, and others. However, across different websites, there is a wide variety of HTML elements (like ,

, ,

, etc.) typically used to render these blocks that makes their automatic extraction a challenge. Most of the current research has focused on extracting product specifications from tables and lists and, therefore, suffers from recall when applied to a large-scale extraction setting. In this paper, we present a product specification extraction approach that goes beyond tables or lists and generalizes across the diverse HTML elements used for rendering specification blocks. Using a combination of hand-coded features and deep learned spatial and token features, we first identify the specification blocks on a product page. We then extract the product attribute-value pairs from these blocks following an approach inspired by wrapper induction. We created a labeled dataset of product specifications extracted from 14,111 diverse specification blocks taken from a range of different product websites. Our experiments show the efficacy of our approach compared to the current specification extraction models and support our claim about its application to large-scale product specification extraction.

关键词： Information Retrieval data mining Extraction Text Classification

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website-Level data Extraction 1

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5th international conference on web Information Systems and Technologies

作者： Li, Jianqiang Zhao, Yu NEC Labs China Beijing 100084 Peoples R China

ISBN: (数字)9783642124365

ISBN: (纸本)9783642124358

this paper proposes a website-level data extraction approach to identify the object relevant information distributed across multiple web pages. Page-level data extraction is widely studied with assumption that each input web page contains multiple data records of interested objects. However, in many cases for web mining, the multiple pages describing an object are sparsely distributed in a website. It makes page-level solutions no longer applicable. We exploit the hierarchy model of websites for web page organization to solve the problem of website-level data extraction. A new resource, the Hierarchical Navigation Path (HNP), which can be discovered from the website structure, is introduced for object relevant web page filtering. the found web pages are clustered using the URL and semantic hyperlink analysis, and then the entry page and the detailed profile pages of each object are identified. the empirical experiments show the effectiveness of the proposed approach.

关键词： web mining Vertical web search web page clustering

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