Exploring Users' Perception of Collaborative Explanation Styles

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization

Zanker

Rook

et al. 2018

Self Cite

Collaborative filtering systems heavily depend on user feedback expressed in product ratings to select and rank items to recommend. These summary statistics of rating values carry two important descriptors about the assessed items, namely the total number of ratings and the mean rating value. In this study we explore how these two signals influence the decisions of online users based on choice-based conjoint experiments. Results show that users are more inclined to follow the mean indicator as opposed to the total number of ratings. Empirical results can serve as an input to developing algorithms that foster items with a, consequently, higher probability of choice based on their rating summarizations or their explainability due to these ratings when ranking recommendations.

“…Detailed results and an extensive discussion is provided in [1]. There was a clear and statistically significant preference relation over the three levels for mean rating values.…”

Section: Resultsmentioning

confidence: 87%

Section: Resultsmentioning

confidence: 99%

“…The design was generated and evaluated using SAS MktEx macros [7]. The SAS code for replicating and evaluating the survey is accessible for download 1 .…”

Section: Methodology and Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring Users' Perception of Rating Summary Statistics

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization

Zanker

Rook

et al. 2018

Self Cite

“…This confirmed that J-shaped rating distributions do indeed occur in tourism data. The number of ratings was set at 20 and 80, since prior work reported that participants clearly notice the difference between these levels (Coba et al 2018a). Mean rating values between 3.6 and 4.0 were found for many rated items in our real-life Tripadvisor data sets (see Fig.…”

Section: Attribute Selection and Experimental Designmentioning

confidence: 99%

Choosing between hotels: impact of bimodal rating summary statistics and maximizing behavioral tendency

Rook

Zanker

2019

Inf Technol Tourism

Self Cite

Rating summary statistics are basic aggregations that reflect users' assessments of experienced products and services in numerical form. Thus far, scholars primarily investigated textual reviews, but dedicated considerably less time and effort exploring the potential impact of plain rating summary statistics on people's choice behavior. Notwithstanding their fundamental nature, however, rating summary statistics also are relevant to electronic commerce in general, and to e-tourism in particular. In this work, we attempted to fill this void, by exploring the effects of different types of rating attributes (the mean rating value, the overall number of ratings, and the bimodality of rating distributions) on hotel choice behavior. We also investigated whether individual differences in the cause of people's maximizing behavioral tendency moderated the effect of rating summary statistics on hotel choice behavior. Results of an eye-tracked conjoint experiment show that people's high or low on decision difficulty as the cause of maximization determined whether and how rating summary statistics have an impact on the choice between hotels. Implications for the tourism and hospitality domain are addressed.

A historical perspective of explainable Artificial Intelligence

Confalonieri

Wagner

et al. 2020

WIREs Data Min & Knowl

Self Cite

186

Explainability in Artificial Intelligence (AI) has been revived as a topic of active research by the need of conveying safety and trust to users in the “how” and “why” of automated decision‐making in different applications such as autonomous driving, medical diagnosis, or banking and finance. While explainability in AI has recently received significant attention, the origins of this line of work go back several decades to when AI systems were mainly developed as (knowledge‐based) expert systems. Since then, the definition, understanding, and implementation of explainability have been picked up in several lines of research work, namely, expert systems, machine learning, recommender systems, and in approaches to neural‐symbolic learning and reasoning, mostly happening during different periods of AI history. In this article, we present a historical perspective of Explainable Artificial Intelligence. We discuss how explainability was mainly conceived in the past, how it is understood in the present and, how it might be understood in the future. We conclude the article by proposing criteria for explanations that we believe will play a crucial role in the development of human‐understandable explainable systems. This article is categorized under: Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Artificial Intelligence