Authors:
Nathalia Cezar
1
;
Isabela Gasparini
1
;
Daniel Lichtnow
2
;
Gabriel Lunardi
2
and
José Moreira de Oliveira
3
Affiliations:
1
Universidade do Estado de Santa Catarina (UDESC), R. Paulo Malschitzki 200, Joinville, Brazil
;
2
Universidade Federal de Santa Maria (UFSM), Av. Roraima 1000, Santa Maria, Brazil
;
3
Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
Keyword(s):
Recommender Systems, Cold Start, New User Cold Start.
Abstract:
Recommender Systems are designed to provide personalized item recommendations to users based on their preferences and behavioral patterns, aiming to suggest items that align with their interests and profile. In Recommender Systems, a common issue arises when the user’s profile is not adequately characterized, particularly at the initial stages of using the system. This issue has persisted in Recommender Systems since its inception, commonly known as Cold Start. The Cold Start issue, which impacts new users, is called User Cold Start. Through a systematic literature mapping, this paper identifies strategies to minimize User Cold Start without reliance on external sources (such as social networks) or user demographic data for initializing the profile of new users. The systematic literature mapping results present strategies aimed at mitigating the User Cold Start Problem, serving as a foundational resource for further enhancements or novel proposals beyond those identified in the revie
w. Thus, the goal of this work is to understand how to create an initial user profile before any prior interaction and without using external sources in the recommender system.
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