Pozo, M.; Chiky, R.; Meziane, F.; Métais, E. Exploiting Past Users’ Interests and Predictions in an Active Learning Method for Dealing with Cold Start in Recommender Systems. Informatics2018, 5, 35.
Pozo, M.; Chiky, R.; Meziane, F.; Métais, E. Exploiting Past Users’ Interests and Predictions in an Active Learning Method for Dealing with Cold Start in Recommender Systems. Informatics 2018, 5, 35.
Pozo, M.; Chiky, R.; Meziane, F.; Métais, E. Exploiting Past Users’ Interests and Predictions in an Active Learning Method for Dealing with Cold Start in Recommender Systems. Informatics2018, 5, 35.
Pozo, M.; Chiky, R.; Meziane, F.; Métais, E. Exploiting Past Users’ Interests and Predictions in an Active Learning Method for Dealing with Cold Start in Recommender Systems. Informatics 2018, 5, 35.
Abstract
This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the new users to interact with the system by presenting them with a questionnaire that aim to understand their preferences to the related items. Example of questions may include "do you like this book?" and the users answer,"yes", "no", "I have not read it (unknown)", will reflect the degree of interest for the item by the users. As a consequence, the system can learn the users' preferences from these answers. The goal of active learning is to correctly choose the questions (items) for users. Thus it is necessary to personalize the questionnaires to retrieve the maximum information by avoiding "unknown" answers. In this paper, we propose an active learning technique that exploits past users' interests and past users' predictions in order to identify the best questions to ask.
Keywords
Cold start, Recommender systems, Active learning, Collaborative filtering
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
