A novel time-aware food recommender-system based on deep learning and graph clustering
Food recommender-systems are considered an effective tool to help users adjust their
eating habits and achieve a healthier diet. This paper aims to develop a new hybrid food …
eating habits and achieve a healthier diet. This paper aims to develop a new hybrid food …
[HTML][HTML] Towards psychology-aware preference construction in recommender systems: Overview and research issues
User preferences are a crucial input needed by recommender systems to determine relevant
items. In single-shot recommendation scenarios such as content-based filtering and …
items. In single-shot recommendation scenarios such as content-based filtering and …
A survey of reinforcement learning from human feedback
Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …
(RL) that learns from human feedback instead of relying on an engineered reward function …
[HTML][HTML] Recommender systems for sustainability: overview and research issues
A Felfernig, M Wundara, TNT Tran… - Frontiers in big …, 2023 - frontiersin.org
Sustainability development goals (SDGs) are regarded as a universal call to action with the
overall objectives of planet protection, ending of poverty, and ensuring peace and prosperity …
overall objectives of planet protection, ending of poverty, and ensuring peace and prosperity …
CRS-Que: A User-centric Evaluation Framework for Conversational Recommender Systems
An increasing number of recommendation systems try to enhance the overall user
experience by incorporating conversational interaction. However, evaluating conversational …
experience by incorporating conversational interaction. However, evaluating conversational …
User needs for explanations of recommendations: In-depth analyses of the role of item domain and personal characteristics
Explanations can be provided with different goals, such as clarifying how the system works,
how well the recommended item meets the user's preferences, and how an explanation …
how well the recommended item meets the user's preferences, and how an explanation …
[HTML][HTML] Bias assessment approaches for addressing user-centered fairness in GNN-based recommender systems
In today's technology-driven society, many decisions are made based on the results
provided by machine learning algorithms. It is widely known that the models generated by …
provided by machine learning algorithms. It is widely known that the models generated by …
Towards the design of user-centric strategy recommendation systems for collaborative Human–AI tasks
Artificial Intelligence is being employed by humans to collaboratively solve complicated
tasks for search and rescue, manufacturing, etc. Efficient teamwork can be achieved by …
tasks for search and rescue, manufacturing, etc. Efficient teamwork can be achieved by …
Navigating Serendipity-An Experimental User Study On The Interplay of Trust and Serendipity In Recommender Systems
Recommender systems play a crucial role in our daily lives, constantly evolving to meet the
diverse needs of users. As the pursuit of improved user experiences continues, metrics such …
diverse needs of users. As the pursuit of improved user experiences continues, metrics such …
Investigating the Potential of Group Recommendation Systems As a Medium of Social Interactions: A Case of Spotify Blend Experiences between Two Users
Designing user experiences for group recommendation systems (GRS) is challenging,
requiring a nuanced understanding of the influence of social interactions between users …
requiring a nuanced understanding of the influence of social interactions between users …