Recent studies have demonstrated that cross-domain recommendation (CDR) effectively addresses the cold-start problem. Most approaches rely on transfer functions to generate user representations from the source to the target domain. Although these methods substantially enhance recommendation performance, they exhibit certain limitations, notably the frequent oversight of similarities in user preferences, which can offer critical insights for training transfer functions. Moreover, existing methods typically derive user preferences from historical purchase records or reviews, without considering that preferences operate at three distinct levels: category, brand, and aspect, each influencing decision-making differently. This paper proposes a model that integrates the preferences from coarse to fine levels to improve recommendations for cold-start users. The model leverages historical data from the source domain and external memory networks to generate user representations across different preference levels. A meta-network then transfers these representations to the target domain, where user-item ratings are predicted by aggregating the diverse representations. Experimental results demonstrate that our model outperforms state-of-the-art approaches in addressing the cold-start problem on three CDR tasks.

Aspect category sentiment analysis (ACSA) aims to simultaneously detect aspect categories and their corresponding sentiment polarities (category-sentiment pairs). Some recent studies have used pre-trained generative models to complete ACSA and achieved good results. However, for ACSA, generative models still face three challenges. First, addressing the missing predictions in ACSA is crucial, which involves accurately predicting all category-sentiment pairs within a sentence. Second, category-sentiment pairs are inherently a disordered set. Consequently, the model incurs a penalty even when its predictions are correct, but the predicted order is inconsistent with the ground truths. Third, different aspect categories should focus on relevant sentiment words, and the polarity of the aspect category should be the aggregation of the polarities of these sentiment words. This paper proposes a hierarchical generative model with a coverage mechanism using sequence-to-set learning to tackle all three challenges simultaneously. Our model’s superior performance is demonstrated through extensive experiments conducted on several datasets.