PURS: Personalized Unexpected Recommender System for Improving User Satisfaction

5 June 2021

Papers citing "PURS: Personalized Unexpected Recommender System for Improving User Satisfaction"

9 / 9 papers shown

Title
A Serendipitous Recommendation System Considering User Curiosity Zhelin Xu Atsushi Matsumura HAI 36 0 0 09 Apr 2025
Food Recommendation With Balancing Comfort and Curiosity Yuto Sakai Qiang Ma 57 0 0 24 Mar 2025
Artworks Reimagined: Exploring Human-AI Co-Creation through Body Prompting J. Oppenlaender Hannah Johnston Johanna M. Silvennoinen Helena Barranha 111 1 0 10 Aug 2024
Algorithmic Fairness: A Tolerance Perspective Renqiang Luo Tao Tang Xiwei Xu Jiaying Liu Chengpei Xu Leo Yu Zhang Wei Xiang Chengqi Zhang FaML 99 0 0 26 Apr 2024
What Are We Optimizing For? A Human-centric Evaluation of Deep Learning-based Movie Recommenders Ruixuan Sun Xinyi Wu A K Akella Ruoyan Kong Bart Knijnenburg Joseph Konstan 82 1 0 21 Jan 2024
Extraction of Atypical Aspects from Customer Reviews: Datasets and Experiments with Language Models Smita Nannaware Erfan Al-Hossami Razvan Bunescu 33 0 0 05 Nov 2023
Topic-Level Bayesian Surprise and Serendipity for Recommender Systems Tonmoy Hasan Razvan Bunescu BDL 48 2 0 11 Aug 2023
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction Pan Li Zhichao Jiang Maofei Que Yao Hu Alexander Tuzhilin LRM 29 56 0 05 Jun 2021
Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations Pan Li Alexander Tuzhilin 61 53 0 17 Apr 2021