Neural Basket Embedding for Sequential Recommendation

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

čeština

Název v původním jazyce

Neural Basket Embedding for Sequential Recommendation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Neural Basket Embedding for Sequential Recommendation

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA18-18080S" target="_blank" >GA18-18080S: Objevování znalostí v datech o aktivitě člověka založené na fúzi</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

RecSys '21: Fifteenth ACM Conference on Recommender Systems

ISBN

978-1-4503-8458-2

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

878-883

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Amsterdam

Datum konání akce

27. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000744461300141