Improving recommendation diversity and serendipity with an ontology-based algorithm for cold start environments

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F23%3A00374422" target="_blank" >RIV/68407700:21240/23:00374422 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/s41060-023-00418-4" target="_blank" >https://doi.org/10.1007/s41060-023-00418-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s41060-023-00418-4" target="_blank" >10.1007/s41060-023-00418-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving recommendation diversity and serendipity with an ontology-based algorithm for cold start environments

Popis výsledku v původním jazyce

Every real-life environments where users interact with items (products, films, research expert profiles) have several development phases. In the Cold-start phase, there are almost no interactions among users and items content-based recommendation systems (RS) can only recommend based on matching the attributes of the items. In the transition state, items start to collect user interactions but still a significant number of items have too small number of interactions, RS does not allow users to discover cold items. In a regular state, where most of the items in the system have enough interactions, the recommendations often suffer from low diversity of the items within a single recommendation. This article proposes a general recommendation algorithm based on Ontological-similarity, which is designed to address all the above problems. Our experiments show that recommendations generated by our approach are consistently better in all environment development phases and increase the success rate of recommendations by almost 50% measured using ontology-aware recall, which is also introduced in this article.

Název v anglickém jazyce

Improving recommendation diversity and serendipity with an ontology-based algorithm for cold start environments

Popis výsledku anglicky

Every real-life environments where users interact with items (products, films, research expert profiles) have several development phases. In the Cold-start phase, there are almost no interactions among users and items content-based recommendation systems (RS) can only recommend based on matching the attributes of the items. In the transition state, items start to collect user interactions but still a significant number of items have too small number of interactions, RS does not allow users to discover cold items. In a regular state, where most of the items in the system have enough interactions, the recommendations often suffer from low diversity of the items within a single recommendation. This article proposes a general recommendation algorithm based on Ontological-similarity, which is designed to address all the above problems. Our experiments show that recommendations generated by our approach are consistently better in all environment development phases and increase the success rate of recommendations by almost 50% measured using ontology-aware recall, which is also introduced in this article.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

International Journal of Data Science and Analytics

ISSN

2364-415X

e-ISSN

—

Svazek periodika

2023

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

1000

Strana od-do

—

Kód UT WoS článku

001040548100002

EID výsledku v databázi Scopus

2-s2.0-85175100818