Li Chen

TL;DR: A unifying evaluation framework, called ResQue (Recommender systems' Quality of user experience), which aimed at measuring the qualities of the recommended items, the system's usability, usefulness, interface and interaction qualities, users' satisfaction with the systems, and the influence of these qualities on users' behavioral intentions.

TL;DR: This work proposes Session-based Temporal Graph (STG) which simultaneously models users' long-term and short-term preferences over time and proposes a novel recommendation algorithm Injected Preference Fusion (IPF) and extends the personalized Random Walk for temporal recommendation.

TL;DR: Results show that at individual stock, sector and index levels, the models with sentiment analysis outperform the bag-of-words model in both validation set and independent testing set, and the models which use sentiment polarity cannot provide useful predictions.

TL;DR: This article provides a comprehensive overview of how the review elements have been exploited to improve standard content-based recommending, collaborative filtering, and preference-based product ranking techniques and classifies state-of-the-art studies into two principal branches: review-based user profile building and review- based product profile building.

TL;DR: This paper surveys the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS’s ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms.