Titlebook: Database Systems for Advanced Applications; 26th International C Christian S. Jensen,Ee-Peng Lim,Chih-Ya Shen Conference proceedings 2021 T

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Gated Sequential Recommendation System with Social and Textual Information Under Dynamic Contextspapers leverage abundant data from heterogeneous information sources to grasp diverse preferences and improve overall accuracy. Some noticeable papers proposed to extract users’ preference from information along with ratings such as reviews or social relations. However, their combinations are genera

暗諷

SRecGAN: Pairwise Adversarial Training for Sequential Recommendationectiveness for such a task by maximizing the margin between observed and unobserved interactions. However, there exist unobserved positive items that are very likely to be selected in the future. Treating those items as negative leads astray and poses a limitation to further exploiting its potential

Carbon-Monoxide

SSRGAN: A Generative Adversarial Network for Streaming Sequential Recommendationonological order. Although a few streaming update strategies have been developed, they cannot be applied in sequential recommendation, because they can hardly capture the long-term user preference only by updating the model with random sampled new instances. Besides, some latent information is ignor

Respond

Topological Interpretable Multi-scale Sequential Recommendation, short or mid-term interest. The multi-scale modeling of user interest in an interpretable way poses a great challenge in sequential recommendation. Hence, we propose a topological data analysis based framework to model target items’ explicit dependency on previous items or item chunks with differe

Maximizer

不能仁慈

Semi-supervised Factorization Machines for Review-Aware Recommendation when the interaction data is sparse. However, existing solutions to review-aware recommendation only focus on learning more informative features from reviews, yet ignore the insufficient number of training examples, resulting in limited performance improvements. To this end, we propose a co-trainin

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STRIA

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Knowledge-Aware Hypergraph Neural Network for Recommender Systemsfiltering in recommender systems. However, most of the existing KG-based recommendation models suffer from the following drawbacks, i.e., insufficient modeling of high-order correlations among users, items, and entities, and simple aggregation strategies which fail to preserve the relational informa

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