Sequential Recommendation with Collaborative Explanation via Mutual Information Maximization
Y Yu, K Sugiyama, A Jatowt - Proceedings of the 47th International ACM …, 2024 - dl.acm.org
Current research on explaining sequential recommendations lacks reliable benchmarks and
quantitative metrics, making it difficult to compare explanation performance between …
quantitative metrics, making it difficult to compare explanation performance between …
Interpreting Stress Detection Models using SHAP and Attention for MuSe-Stress 2022
Understanding emotional reactions, especially stress, during job interviews holds significant
implications for assessing the well-being of candidates and tailoring feedback. However …
implications for assessing the well-being of candidates and tailoring feedback. However …
Category-aware Graph Neural Network for Session-based Recommendation
R Chen, P Ma, Y Zhu, Q Chen - 2022 IEEE 28th International …, 2023 - ieeexplore.ieee.org
Session-based recommendation (SBR) aims to predict which item will be clicked next for the
current session. Many studies have shown the advantages of utilizing graph neural networks …
current session. Many studies have shown the advantages of utilizing graph neural networks …
Laser: Parameter-Efficient LLM Bi-Tuning for Sequential Recommendation with Collaborative Information
Sequential recommender systems are essential for discerning user preferences from
historical interactions and facilitating targeted recommendations. Recent innovations …
historical interactions and facilitating targeted recommendations. Recent innovations …
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation
Reinforcement learning (RL) has been widely applied in recommendation systems due to its
potential in optimizing the long-term engagement of users. From the perspective of RL …
potential in optimizing the long-term engagement of users. From the perspective of RL …
CETD: Counterfactual Explanations by Considering Temporal Dependencies in Sequential Recommendation
M He, B An, J Wang, H Wen - Applied Sciences, 2023 - mdpi.com
Providing interpretable explanations can notably enhance users' confidence and satisfaction
with regard to recommender systems. Counterfactual explanations demonstrate remarkable …
with regard to recommender systems. Counterfactual explanations demonstrate remarkable …
Multi-Grained Preference Enhanced Transformer for Multi-Behavior Sequential Recommendation
C He, Y Liu, Q Li, W Wang, X Fu, X Fu, C Hong… - arXiv preprint arXiv …, 2024 - arxiv.org
Sequential recommendation (SR) aims to predict the next purchasing item according to
users' dynamic preference learned from their historical user-item interactions. To improve …
users' dynamic preference learned from their historical user-item interactions. To improve …
Pattern-wise Transparent Sequential Recommendation
K Ma, C Xu, Z Chen, W Zhang - arXiv preprint arXiv:2402.11480, 2024 - arxiv.org
A transparent decision-making process is essential for developing reliable and trustworthy
recommender systems. For sequential recommendation, it means that the model can identify …
recommender systems. For sequential recommendation, it means that the model can identify …
Enhancing recommender ensemble by estimating input fitness
Recommendation system is designed to tackle the information overload problem. The
performance of a single recommendation system can be significantly improved if ensemble …
performance of a single recommendation system can be significantly improved if ensemble …
Popularity Estimation and New Bundle Generation using Content and Context based Embeddings
A Nayak, P NJ, S Keshav, R Reddy… - arXiv preprint arXiv …, 2024 - arxiv.org
Recommender systems create enormous value for businesses and their consumers. They
increase revenue for businesses while improving the consumer experience by …
increase revenue for businesses while improving the consumer experience by …