Neural Basket Embedding for Sequential Recommendation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F21%3A00351860" target="_blank" >RIV/68407700:21240/21:00351860 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1145/3460231.3473896" target="_blank" >https://doi.org/10.1145/3460231.3473896</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3460231.3473896" target="_blank" >10.1145/3460231.3473896</a>

Result language

čeština

Original language name

Neural Basket Embedding for Sequential Recommendation

Original language description

Next basket prediction from historical purchases is quite a complex task, even for e-commerce datasets with a low number of items that are being purchased repeatedly. Neural approaches are not much better in predicting next purchases than simple heuristics. This paper focuses on the challenge of how to encode baskets into efficient neural embedding with low reconstruction error while maintaining the similarity of baskets in the latent space. In our representation, replacing a product with a similar product or increasing quantity will not change the embedding of the basket much. We believe that good basket representation is critical for subsequent prediction. Our analysis shows that state-of-the-art next basket prediction approaches have limitations in their representation of baskets. We would like to focus on this aspect in our future research.

Czech name

Neural Basket Embedding for Sequential Recommendation

Czech description

Next basket prediction from historical purchases is quite a complex task, even for e-commerce datasets with a low number of items that are being purchased repeatedly. Neural approaches are not much better in predicting next purchases than simple heuristics. This paper focuses on the challenge of how to encode baskets into efficient neural embedding with low reconstruction error while maintaining the similarity of baskets in the latent space. In our representation, replacing a product with a similar product or increasing quantity will not change the embedding of the basket much. We believe that good basket representation is critical for subsequent prediction. Our analysis shows that state-of-the-art next basket prediction approaches have limitations in their representation of baskets. We would like to focus on this aspect in our future research.

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

RecSys '21: Fifteenth ACM Conference on Recommender Systems

ISBN

978-1-4503-8458-2

ISSN

—

e-ISSN

—

Number of pages

6

Pages from-to

878-883

Publisher name

Association for Computing Machinery

Place of publication

New York

Event location

Amsterdam

Event date

Sep 27, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000744461300141