Offline evaluation of the serendipity in recommendation systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00363087" target="_blank" >RIV/68407700:21240/22:00363087 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10000782" target="_blank" >https://ieeexplore.ieee.org/document/10000782</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CSIT56902.2022.10000782" target="_blank" >10.1109/CSIT56902.2022.10000782</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Offline evaluation of the serendipity in recommendation systems

Popis výsledku v původním jazyce

Offline optimization of recommender systems is a difficult task. Popular optimization criteria such as RMSE, Recall, and NDCG do not correlate much with online performance, especially when the recommendation algorithm is largely different from the one used to generate the offline data. An exciting direction of research to mitigate this problem is to use more robust optimization criteria. Serendipity is reported to be a promising proxy. However, more variants exist, and it is unclear whether they can be used as a single criterion to optimize. This paper analyzes how serendipity relates to other optimization criteria for three different recommendation algorithms. Based on our findings, we propose to modify the way serendipity is computed. We conduct experiments using three collaborative filtering algorithms: K-Nearest Neighbors, Matrix Factorization, and Embarrassingly Shallow Autoencoder (EASE). We also employ and evaluate the ensemble learning approach and analyze the importance of the individual components of serendipity.

Název v anglickém jazyce

Offline evaluation of the serendipity in recommendation systems

Popis výsledku anglicky

Offline optimization of recommender systems is a difficult task. Popular optimization criteria such as RMSE, Recall, and NDCG do not correlate much with online performance, especially when the recommendation algorithm is largely different from the one used to generate the offline data. An exciting direction of research to mitigate this problem is to use more robust optimization criteria. Serendipity is reported to be a promising proxy. However, more variants exist, and it is unclear whether they can be used as a single criterion to optimize. This paper analyzes how serendipity relates to other optimization criteria for three different recommendation algorithms. Based on our findings, we propose to modify the way serendipity is computed. We conduct experiments using three collaborative filtering algorithms: K-Nearest Neighbors, Matrix Factorization, and Embarrassingly Shallow Autoencoder (EASE). We also employ and evaluate the ensemble learning approach and analyze the importance of the individual components of serendipity.

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

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

IEEE 17th International Conference on Computer Science and Information Technologies

ISBN

979-8-3503-3431-9

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

597-601

Název nakladatele

IEEE

Místo vydání

Dortmund

Místo konání akce

Lvov

Datum konání akce

10. 11. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000927642900140