On-demand Personalized Explanation for Transparent Recommendation

The literature on explainable recommendations is already rich. In this paper, we aim to shed light on an aspect that remains under-explored in this area of research, namely providing personalized explanations. To address this gap, we developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides on-demand personalized explanations with varying levels of detail to meet the demands of different types of end-users. The results of a preliminary qualitative user study demonstrated potential benefits in terms of user satisfaction with the explainable recommender system. Our work would contribute to the literature on explainable recommendation by exploring the potential of on-demand personalized explanations, and contribute to the practice by offering suggestions for the design and appropriate use of personalized explanation interfaces in recommender systems.

[1]  Tim Miller,et al.  Explanation in Artificial Intelligence: Insights from the Social Sciences , 2017, Artif. Intell..

[2]  Pasquale Lops,et al.  ExpLOD: A Framework for Explaining Recommendations based on the Linked Open Data Cloud , 2016, RecSys.

[3]  Amit Dhurandhar,et al.  One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques , 2019, ArXiv.

[4]  Marshall Scott Poole,et al.  What Is Personalization? Perspectives on the Design and Implementation of Personalization in Information Systems , 2006, J. Organ. Comput. Electron. Commer..

[5]  Lise Getoor,et al.  Personalized explanations for hybrid recommender systems , 2019, IUI.

[6]  Izak Benbasat,et al.  Do Users Always Want to Know More? Investigating the Relationship between System Transparency and Users' Trust in Advice-Giving Systems , 2019, ECIS.

[7]  Mouzhi Ge,et al.  How should I explain? A comparison of different explanation types for recommender systems , 2014, Int. J. Hum. Comput. Stud..

[8]  Nava Tintarev,et al.  Evaluating the effectiveness of explanations for recommender systems , 2012, User Modeling and User-Adapted Interaction.

[9]  Mária Bieliková,et al.  Towards understandable personalized recommendations: Hybrid explanations , 2019, Comput. Sci. Inf. Syst..

[10]  Niklas Kühl,et al.  Do you comply with AI? - Personalized explanations of learning algorithms and their impact on employees' compliance behavior , 2020, ArXiv.

[11]  JannachDietmar,et al.  A systematic review and taxonomy of explanations in decision support and recommender systems , 2017 .

[12]  Martijn Millecamp,et al.  To explain or not to explain: the effects of personal characteristics when explaining music recommendations , 2019, IUI.

[13]  Eric D. Ragan,et al.  A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems , 2018, ACM Trans. Interact. Intell. Syst..

[14]  Judith Masthoff,et al.  Explaining Recommendations: Design and Evaluation , 2015, Recommender Systems Handbook.

[15]  Dietmar Jannach,et al.  A systematic review and taxonomy of explanations in decision support and recommender systems , 2017, User Modeling and User-Adapted Interaction.

[16]  Barry Smyth,et al.  Why I like it: multi-task learning for recommendation and explanation , 2018, RecSys.

[17]  Alexander Jung,et al.  An Information-Theoretic Approach to Personalized Explainable Machine Learning , 2020, IEEE Signal Processing Letters.

[18]  Xu Chen,et al.  Explainable Recommendation: A Survey and New Perspectives , 2018, Found. Trends Inf. Retr..

[19]  James McInerney,et al.  Explore, exploit, and explain: personalizing explainable recommendations with bandits , 2018, RecSys.

[20]  Lise Getoor,et al.  Generating and Understanding Personalized Explanations in Hybrid Recommender Systems , 2020, ACM Trans. Interact. Intell. Syst..

[21]  Peter Brusilovsky,et al.  User modeling and user adapted interaction , 2001 .

[22]  F. Maxwell Harper,et al.  Crowd-Based Personalized Natural Language Explanations for Recommendations , 2016, RecSys.

[23]  Jürgen Ziegler,et al.  Explaining Recommendations by Means of User Reviews , 2018, IUI Workshops.