Sangam: A Confluence of Knowledge Streams

ANTHEM: Attentive Hyperbolic Entity Model for Product Search

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dc.creator Choudhary, Nurendra
dc.creator Rao, Nikhil
dc.creator Katariya, Sumeet
dc.creator Subbian, Karthik
dc.creator Reddy, Chandan K.
dc.date 2022-10-19T16:55:46Z
dc.date 2022-10-19T16:55:46Z
dc.date 2022-02-11
dc.date 2022-10-19T15:08:04Z
dc.date.accessioned 2023-03-01T18:54:23Z
dc.date.available 2023-03-01T18:54:23Z
dc.identifier http://hdl.handle.net/10919/112213
dc.identifier https://doi.org/10.1145/3488560.3498456
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/281825
dc.description Product search is a fundamentally challenging problem due to the large-size of product catalogues and the complexity of extracting semantic information from products. In addition to this, the blackbox nature of most search systems also hamper a smooth customer experience. Current approaches in this area utilize lexical and semantic product information to match user queries against products. However, these models lack (i) a hierarchical query representation, (ii) a mechanism to detect and capture inter-entity relationships within a query, and (iii) a query composition method specific to e-commerce domain. To address these challenges, in this paper, we propose an AtteNTive Hyperbolic Entity Model (ANTHEM), a novel attention-based product search framework that models query entities as two-vector hyperboloids, learns inter-entity intersections and utilizes attention to unionize individual entities and inter-entity intersections to predict product matches from the search space. ANTHEM utilizes the first and second vector of hyperboloids to determine the query’s semantic position and to tune its surrounding search volume, respectively. The attention networks capture the significance of intra-entity and inter-entity intersections to the final query space. Additionally, we provide a mechanism to comprehend ANTHEM and understand the significance of query entities towards the final resultant products. We evaluate the performance of our model on real data collected from popular e-commerce sites. Our experimental study on the offline data demonstrates compelling evidence of ANTHEM’s superior performance over state-of-the-art product search methods with an improvement of more than 10% on various metrics. We also demonstrate the quality of ANTHEM’s query encoder using a query matching task.
dc.description Published version
dc.format application/pdf
dc.format application/pdf
dc.language en
dc.publisher ACM
dc.rights Creative Commons Attribution 4.0 International
dc.rights http://creativecommons.org/licenses/by/4.0/
dc.rights The author(s)
dc.title ANTHEM: Attentive Hyperbolic Entity Model for Product Search
dc.type Article - Refereed
dc.type Text


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