In recent years, sequential recommendation (SR) has gained significant attention in the recommender systems community. However, most previous works only consider the (user, item, timestep) interaction sequences, which limits the recommendation performance. To overcome this limitation, some studies have utilized user reviews to enrich the understanding of user preferences. However, existing review-based sequential recommendation (RBSR) methods only use either a user’s review on items or an item’s reviews by users, overlooking their complementary nature. In addition, most existing RBSR methods use a simple dot-product operation between the embeddings of a user and the candidate items for scoring, which may not adequately capture the complex relationships among the item sequence, review sequence and candidate items. To release the potential of RBSR, we propose a novel model called cascaded cross attention (CCA), which utilizes aggregated reviews to compensate for the information that is lacking in individual reviews. Moreover, we propose a cascaded cross-attention layer to better capture the dependency intra a sequence and the relationships between a sequence and the candidate items. Extensive experimental results on three public datasets demonstrate that our CCA outperforms the state-of-the-art methods. Additionally, the case study and visualization results showcase high interpretability of our CCA.

Existing legal judgment prediction methods usually only consider one single case fact description as input, which may not fully utilize the information in the data such as case relations and frequency. In this paper, we propose a new perspective that introduces some contrastive case relations to construct case triples as input, and a corresponding judgment prediction framework with case triples modeling (CTM). Our CTM can more effectively utilize beneficial information to refine the encoding and decoding processes through three customized modules, including the case triple module, the relational attention module, and the category decoder module. Finally, we conduct extensive experiments on two public datasets to verify the effectiveness of our CTM, including overall evaluation, compatibility analysis, ablation studies, analysis of gain source and visualization of case representations.