Overcoming the Cold-Start Problem in Recommendation Systems with Ontologies and Knowledge Graphs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F23%3A00374427" target="_blank" >RIV/68407700:21240/23:00374427 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-42941-5_52" target="_blank" >https://doi.org/10.1007/978-3-031-42941-5_52</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-42941-5_52" target="_blank" >10.1007/978-3-031-42941-5_52</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Overcoming the Cold-Start Problem in Recommendation Systems with Ontologies and Knowledge Graphs

Popis výsledku v původním jazyce

Many recommendation systems struggle with the cold-start problem, especially in the early stages of a new application, when there are few active users and limited interactions. Traditional approaches like Collaborative Filtering cannot provide enough recommendations, and text-based methods, while helpful, do not offer sufficient context. This paper argues against the idea that the cold-start phase will eventually disappear and proposes a solution to enhance recommendation performance from the start. We propose using Ontologies and Knowledge Graphs to add a semantic layer to text-based methods and improve the recommendation performance in cold-start scenarios. Our approach uses ontologies to generate a knowledge graph that captures item text attributes’ implicit and explicit characteristics, extending the item profile with similar semantic keywords. We evaluate our method against state-of-the-art text feature extraction techniques and present the results of our experiments.

Název v anglickém jazyce

Overcoming the Cold-Start Problem in Recommendation Systems with Ontologies and Knowledge Graphs

Popis výsledku anglicky

Many recommendation systems struggle with the cold-start problem, especially in the early stages of a new application, when there are few active users and limited interactions. Traditional approaches like Collaborative Filtering cannot provide enough recommendations, and text-based methods, while helpful, do not offer sufficient context. This paper argues against the idea that the cold-start phase will eventually disappear and proposes a solution to enhance recommendation performance from the start. We propose using Ontologies and Knowledge Graphs to add a semantic layer to text-based methods and improve the recommendation performance in cold-start scenarios. Our approach uses ontologies to generate a knowledge graph that captures item text attributes’ implicit and explicit characteristics, extending the item profile with similar semantic keywords. We evaluate our method against state-of-the-art text feature extraction techniques and present the results of our experiments.

Druh

J_ost - Ostatní články v recenzovaných periodicích

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

Communications in Computer and Information Science

ISSN

1865-0929

e-ISSN

—

Svazek periodika

2023

Číslo periodika v rámci svazku

1850

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

13

Strana od-do

591-603

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85172030358