Um dos problemas mais comuns em Sistemas de Recomendação é o famoso Cold Start (i.e. quando não há conhecimento prévio sobre os gostos de alguém que acaba de entrar na plataforma).
Esse paper trás uma perspectiva interessante sobre o assunto.
Abstract: One of the main challenges in Recommender Systems (RSs) is the New User problem which happens when the system has to generate personalised recommendations for a new user whom the system has no information about. Active Learning tries to solve this problem by acquiring user preference data with the maximum quality, and with the minimum acquisition cost. Although there are variety of works in active learning for RSs research area, almost all of them have focused only on the single-domain recommendation scenario. However, several real-world RSs operate in the cross-domain scenario, where the system generates recommendations in the target domain by exploiting user preferences in both the target and auxiliary domains. In such a scenario, the performance of active learning strategies can be significantly influenced and typical active learning strategies may fail to perform properly. In this paper, we address this limitation, by evaluating active learning strategies in a novel evaluation framework, explicitly suited for the cross-domain recommendation scenario. We show that having access to the preferences of the users in the auxiliary domain may have a huge impact on the performance of active learning strategies w.r.t. the classical, single-domain scenario.
Conclusions: In this paper, we have evaluated several widely used active learning strategies adopted to tackle the cold-start problem in a novel usage scenario, i.e., Cross-domain recommendation scenario. In such a case, the user preferences are available not only in the target domain, but also in additional auxiliary domain. Hence, the active learner can exploit such knowledge to better estimate which preferences are more valuable for the system to acquire. Our results have shown that the performance of the considered active learning strategies significantly change in the cross-domain recommendation scenario in comparison to the single-domain recommendation. Hence, the presence of the auxiliary domain may strongly influence the performance of the active learning strategies. Indeed, while a certain active learning strategy performs the best for MAE reduction in the single scenario (i.e., highest-predicted strategy), it actually performs poor in the cross-domain scenario. On the other hand, the strategy with the worst MAE in single-domain scenario (i.e., lowest-predicted strategy) can perform excellent in the cross-domain scenario. This is an interesting observation which indicates the importance of further analysis of these two scenarios in order to better design and develop active learning strategies for them. Our future work includes the further analysis of the AL strategies in other domains such as book, electronic products, tourism, etc. Moreover, we plan to investigate the potential impact of considering different rating prediction models (e.g., context-aware models) on the performance of different active learning strategies.