Product Knowledge Graph Embedding for E-commerce

Xu, Da; Ruan, Chuanwei; Körpeoğlu, Evren; Kumar, Sushant; Achan, Kannan

doi:10.1145/3336191.3371778

Cited by 76 publications

(47 citation statements)

References 23 publications

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“…Wan et al [25] focus in their work on product complementarity, product compatibility, and customer loyalty patterns, and propose a new recommender method that takes these aspects into account. Moreover, structured information about product relationships (e.g., knowledge graphs or product taxonomies) is also often used to improve product recommendations [6,28]. However, most recommender approaches specifically focus on a single shopping domain.…”

Section: Related Workmentioning

confidence: 99%

Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels

Wölbitsch¹,

Hasler²,

Walk³

et al. 2020

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

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Over the course of the last decade, online retailers have demonstrated that knowledge about customer preferences and shopping patterns is an important asset for running a successful business. For example, customer preferences and shopping histories are the foundation for recommender systems that support the search for relevant products to buy online. With the increasing adoption of modern technologies, traditional retailers are able to collect similar data about customer behavior in their stores. For example, smart fitting rooms allow to track interactions of customers with products beyond the scope of a traditional retail store. In this paper we explore how customers of a large international fashion retailer buy products online and in brick-and-mortar stores, and uncover significant differences between the two domains. In particular, we find that online customers frequently focus on buying products from one specific category, whereas customers in brick-and-mortar stores often buy a more diverse range of product types. Further, we investigate products that customers take into fitting rooms, and we find that they frequently deviate from, and complement purchases. Finally, we demonstrate how our findings impact practical applications, illustrated using recommender systems, and discuss how shopping baskets from different domains can be leveraged.

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Section: Related Workmentioning

confidence: 99%

Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels

Wölbitsch¹,

Hasler²,

Walk³

et al. 2020

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

View full text Add to dashboard Cite

show abstract

“…In contrast, Shah et al [31] uses a classifier for the product matching task. Others have used various neuralnetworks based representations of products to improve query to product matching or to create personalized product recommendations [1,33,35].…”

Section: Related Workmentioning

confidence: 99%

Intent-Driven Similarity in E-Commerce Listings

Fuchs¹,

Acriche²,

Hasson³

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Discovering similarities between online listings is a common backend task being used across different downstream experiences in eBay. Our baseline unstructured listing similarity method relies on measuring the semantic textual similarity between the embedding vectors of listing titles. However, we discovered that even with the latest contextualized embedding methods, our similarity fails to give the proper weight to the key tokens in the title that matter. This often results in identifying listing similarities that are not sufficient, which later hurts the downstream experiences. In this paper we present a method we call "Listing2Query", or "L2Q", which uses a Sequence Labeling approach to learn token importance from our users' search queries and on-site behaviour. We used pairs of listing titles and their matching search queries, and leveraged a contextualized character language model, to train L2Q as a bidirectional recurrent neural network to produce token importance weights. We demonstrate that plugging these weights into relatively straightforward listing similarity methods is a simple way to significantly improve the similarity results, even to the extent that it consistently outperforms those created by popular representations such as BERT. Notably, this approach is not reserved to only large online marketplaces but can be generalized to other cases that include a search-driven experience and a recall set of short documents.

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“…al. [10]. In this work, the authors proposed a novel knowledge graph embedding approach for learning the product relations as product knowledge for the electronic commerce domain [10].…”

Section: Knowledge Graph-based Recommendation Systemsmentioning

confidence: 99%

“…[10]. In this work, the authors proposed a novel knowledge graph embedding approach for learning the product relations as product knowledge for the electronic commerce domain [10]. They have constructed a new approach called a self-attention-enhanced distributed representation learning model that was capable of capturing embeddings from customer activity data from electronic commerce websites.…”

Section: Knowledge Graph-based Recommendation Systemsmentioning

confidence: 99%

“…They have constructed a new approach called a self-attention-enhanced distributed representation learning model that was capable of capturing embeddings from customer activity data from electronic commerce websites. The proposed approach was executed on some real-world dataset and the performance was evaluated for knowledge completion, search, ranking and recommendation processes [10]. Another approach that introduced a fine-grained knowledge graph for providing better personalized recommendations called DUSKG [11] was another approach that attempted incorporating multiple types of service data considering their logical relations.…”

Section: Knowledge Graph-based Recommendation Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Knowledge Graph-based Recommendation Systems: The State-of-the-art and Some Future Directions

Sajisha

Anoop

Ansal³

2019

IJMLNCE

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he unprecedented growth of unstructured data poses many challenges in semantic computing, which is an active research area for many years. While unearthing interesting patterns such as entities, relationships, and other metadata are important, it is equally important to represent them in an efficient, easy to access manner. Knowledge Graphs (KGs) are one such mechanism to represent facts extracted from unstructured text. KGs represent entities as nodes and relationships as edges. Such a representation may find applications in many meaning-aware computing applications such as question answering, summarization, etc., to name a few. Very recently, knowledge graph-based recommendation systems have become popular which has many advantages over traditional recommendation engines. This survey is an attempt to summarize and critically evaluate some of the very recent approaches to knowledge graph-based recommendation approaches.

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Product Knowledge Graph Embedding for E-commerce

Cited by 76 publications

References 23 publications

Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels

Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels

Intent-Driven Similarity in E-Commerce Listings

Knowledge Graph-based Recommendation Systems: The State-of-the-art and Some Future Directions

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