Multi-component similarity method for web product duplicate detection

Bezu, Ronald van; Borst, Sjoerd; Rijkse, Rick; Verhagen, Jim; Vandic, Damir; Frăsincar, Flavius

doi:10.1145/2695664.2695818

Cited by 21 publications

(21 citation statements)

References 14 publications

(23 reference statements)

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“…Similar to product classification, typically product linking will make use of product names (e.g., Kannan et al (2011); Gopalakrishnan et al (2012);Vandic et al (2012);van Bezu et al (2015); Shah et al (2018);Tracz et al (2020); Li et al (2020)) and descriptions (e.g., Petrovski et al (2014); Ristoski et al (2018); Li et al (2020)). The difference however, is that the task also makes use of a diverse range of structured product attributes (e.g., van Bezu et al (2015); Shah et al (2018); Petrovski and Bizer (2020); Li et al (2020)), often defined as 'key-value' pairs such as those that can be extracted from product specifications (e.g., product ID, model, brand, manufacturer). Intuitively, offers that have the similar sets of key-value pairs are more likely to match.…”

Section: Product Linkingmentioning

confidence: 99%

“…Feature representation. Again, similar to product classification, broadly speaking, transforming textual metadata into feature representations is typically based on BoW (e.g., Vandic et al (2012);van Bezu et al (2015)), pre-trained word embeddings or language models (e.g., Ristoski et al (2018); Shah et al (2018); Li et al (2020); Peeters et al (2020a);Tracz et al (2020)), or learning word embeddings on-the-spot from the downstream task datasets (e.g., Shah et al (2018)). However, depending on the types of metadata, different methods may be adopted and then combined (Köpcke et al (2012)).…”

Section: Product Linkingmentioning

confidence: 99%

“…For example, structured keyvalue attributes are often kept as-is and compared as a BoW, particularly if the values are short (e.g., product IDs). In van Bezu et al (2015), a concept of 'qgram' was introduced to represent short texts (especially key-value pairs) as a set of character n-grams. Longer texts such as descriptions are better represented using word embeddings or through LMs.…”

Section: Product Linkingmentioning

confidence: 99%

See 2 more Smart Citations

An Exploratory Study on Utilising the Web of Linked Data for Product Data Mining

Zhang¹,

Song²

2021

Preprint

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The Linked Open Data practice has led to a significant growth of structured data on the Web in the last decade. Such structured data describe real-world entities in a machine-readable way, and have created an unprecedented opportunity for research in the field of Natural Language Processing. However, there is a lack of studies on how such data can be used, for what kind of tasks, and to what extent they can be useful for these tasks. This work focuses on the e-commerce domain to explore methods of utilising such structured data to create language resources that may be used for product classification and linking. We process billions of structured data points in the form of RDF n-quads, to create multi-million words of product-related corpora that are later used in three different ways for creating of language resources: training word embedding models, continued pretraining of BERT-like language models, and training Machine Translation models that are used as a proxy to generate product-related keywords. Our evaluation on an extensive set of benchmarks shows word embeddings to be the most reliable and consistent method to improve the accuracy on both tasks (with up to 6.9 percentage points in macro-average F1 on some datasets). The other two methods however, are not as useful. Our analysis shows that this could be due to a number of reasons, including the biased domain representation in the structured data and lack of vocabulary coverage. We share our datasets and discuss how our lessons learned could be taken forward to inform future research in this direction.Keywords linked data • web of data • schema.org • natural language processing • nlp • data mining • product mining

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Section: Product Linkingmentioning

confidence: 99%

Section: Product Linkingmentioning

confidence: 99%

Section: Product Linkingmentioning

confidence: 99%

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An Exploratory Study on Utilising the Web of Linked Data for Product Data Mining

Zhang¹,

Song²

2021

Preprint

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“…The approach is based on several string similarity functions for product matching. The approach is extended by using a hybrid similarity method and hierarchical clustering for matching products from multiple e-shops [1].…”

Section: Product Matchingmentioning

confidence: 99%

A machine learning approach for product matching and categorization

Ristoski

Petrovski

Mika

et al. 2018

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Consumers today have the option to purchase products from thousands of e-shops. However, the completeness of the product specifications and the taxonomies used for organizing the products differ across different e-shops. To improve the consumer experience, approaches for product integration on the Web are needed. In this paper, we present an approach that leverages deep learning techniques in combination with standard classification approaches for product matching and categorization. In our approach we use structured product data as supervision for training feature extraction models able to extract attribute-value pairs from textual product descriptions. To minimize the need for lots of data for supervision, we use neural language models to produce word embeddings from large quantities of publicly available product data marked up with Microdata, which boost the performance of the feature extraction model, thus leading to better product matching and categorization performances. Furthermore, we use a deep Convolutional Neural Network to produce image embeddings from product images, which further improve the results on both tasks.

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“…The approach is first extended in [1], using a hybrid similarity method. Later, the method is extended in [2], where hierarchical clustering is used for matching products from multiple web shops, using the same hybrid similarity method.…”

Section: Related Workmentioning

confidence: 99%

Enriching Product Ads with Metadata from HTML Annotations

Ristoski

Mika

2016

The Semantic Web. Latest Advances and New Domains

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Abstract. Product ads are a popular form of search advertizing offered by major search engines, including Yahoo, Google and Bing. Unlike traditional search ads, product ads include structured product specifications, which allow search engine providers to perform better keyword-based ad retrieval. However, the level of completeness of the product specifications varies and strongly influences the performance of ad retrieval. On the other hand, online shops are increasing adopting semantic markup languages such as Microformats, RDFa and Microdata, to annotate their content, making large amounts of product description data publicly available. In this paper, we present an approach for enriching product ads with structured data extracted from thousands of online shops offering Microdata annotations. In our approach we use structured product ads as supervision for training feature extraction models able to extract attribute-value pairs from unstructured product descriptions. We use these features to identify matching products across different online shops and enrich product ads with the extracted data. Our evaluation on three product categories related to electronics show promising results in terms of enriching product ads with useful product data.

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Multi-component similarity method for web product duplicate detection

Cited by 21 publications

References 14 publications

An Exploratory Study on Utilising the Web of Linked Data for Product Data Mining

An Exploratory Study on Utilising the Web of Linked Data for Product Data Mining

A machine learning approach for product matching and categorization

Enriching Product Ads with Metadata from HTML Annotations

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