User perception of sentiment-integrated critiquing in recommender systems. Issue 121 (January 2019)

• Record Type: Journal Article
• Authors: Chen, Li; Yan, Dongning; Wang, Feng
• Is Part Of: International journal of human-computer studies. Issue 121(2019)
• Page Start: 4
• Page End: 20
• Publication Date: 2019-01
• Subjects: Critiquing-based recommender systems -- Product reviews -- Feature-based sentiment analysis -- E-commerce -- User evaluation; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2017.09.005
• Languages: English
• ISSNs: 1071-5819

Abstract

Highlights: Development of a novel critiquing-based recommender system based on feature sentiments extracted from product reviews. Modeling of user preferences for both static attribute values and feature sentiments. Design and conduction of two experiments on the developed system. Empirical demonstration of the system's effectiveness in enhancing user experiences. Abstract: Critiquing in recommender systems has been accepted as an effective feedback mechanism that allows users to incrementally refine their preferences for product attributes, especially in complex decision environments and high-investment product domains where users' initial preferences are usually uncertain and incomplete. However, the traditional critiquing methods are limited in that they are only based on static attribute values (such as a digital camera's screen size, effectiveness pixels, optical zoom). Considering product reviews contain other customers' sentiments (also called opinions) expressed on some features, in this manuscript, we propose a sentiment-integrated critiquing approach, for helping users to formulate and refine their preferences. Through both before-after and within-subjects experiments, we find that the incorporation of feature sentiments into the critiquing interface can significantly improve users' product knowledge, preference certainty, decision confidence, perceived information usefulness, and purchase intention. The results can hence be constructive for enhancing current critiquing-based recommender systems.