Controlling Spotify Recommendations: Effects of Personal Characteristics on Music Recommender User Interfaces

The "black box'' nature of today's recommender systems raises a number of challenges for users, including a lack of trust and limited user control. Providing more user control is interesting to enable end-users to help steer the recommendation process with additional input and feedback. However, different users may have different preferences with regard to such control. To the best of our knowledge, no research has investigated the effect of personal characteristics on visual control techniques in the music recommendation domain. In this paper, we present results of a user study on the web using two different visualisation techniques (a radar chart and sliders) that allows users to control Spotify recommendations. A within-subject design withLatin Square counterbalancing measures was used for the study. Results indicate that the radar chart helped the participants discover a significantly higher number of new songs compared to the sliders. We also found that users' experience with Spotify had an influence on their interaction with different musical attributes. The participants who used Spotify frequently and users with a high individual musical sophistication interacted with the attributes significantly more with the radar chart compared to the sliders. Individual musical sophistication also had a significant impact on their interaction with the interaction techniques. The participants with high musical sophistication interacted significantly more with the radar chart in comparison to the sliders. Based on the feedback from our participants, we provide design suggestions to further improve user control in music recommendation.

[1]  Rashmi R. Sinha,et al.  The role of transparency in recommender systems , 2002, CHI Extended Abstracts.

[2]  Yu Chen.  CoFeel : Using Emotions to Enhance Social Interaction in Group Recommender Systems , 2012 .

[3]  Gitta Domik,et al.  User modeling for adaptive visualization systems , 1994, Proceedings Visualization '94.

[4]  Cristina Conati,et al.  Towards adaptive information visualization: on the influence of user characteristics , 2012, UMAP.

[5]  André Calero Valdez,et al.  What Should I Read Next? A Personalized Visual Publication Recommender System , 2015, HCI.

[6]  Mark H. Chignell,et al.  Assessment of the effects of user characteristics on mental models of information retrieval systems , 2001, J. Assoc. Inf. Sci. Technol..

[7]  R. Kessels,et al.  The Corsi Block-Tapping Task: Standardization and Normative Data , 2000, Applied neuropsychology.

[8]  Dennis R. Wixon,et al.  CHI '02 Extended Abstracts on Human Factors in Computing Systems , 2002, CHI 2002.

[9]  Jürgen Ziegler,et al.  Blended Recommending: Integrating Interactive Information Filtering and Algorithmic Recommender Techniques , 2015, CHI.

[10]  F. Maxwell Harper,et al.  Letting Users Choose Recommender Algorithms: An Experimental Study , 2015, RecSys.

[11]  Panagiotis Germanakos,et al.  The Effects of Personality Type in User-Centered Appraisal Systems , 2011, HCI.

[12]  Cristina Conati,et al.  Highlighting interventions and user differences: informing adaptive information visualization support , 2014, CHI.

[13]  Mark Sanderson,et al.  The effect of user characteristics on search effectiveness in information retrieval , 2011, Inf. Process. Manag..

[14]  Tobias Höllerer,et al.  LinkedVis: exploring social and semantic career recommendations , 2013, IUI '13.

[15]  Tobias Höllerer,et al.  TasteWeights: a visual interactive hybrid recommender system , 2012, RecSys.

[16]  J. Venn,et al.  . On the diagrammatic and mechanical representation of propositions and reasonings , 2022 .

[17]  Shogo Nishida,et al.  The relation between user intervention and user satisfaction for information recommendation , 2012, SAC '12.

[18]  Michal Kosinski,et al.  The Song Is You , 2016 .

[19]  Cristina Conati,et al.  Evaluating the Impact of User Characteristics and Different Layouts on an Interactive Visualization for Decision Making , 2014, Comput. Graph. Forum.

[20]  V. Braun,et al.  Using thematic analysis in psychology , 2006 .

[21]  Li Chen,et al.  A user-centric evaluation framework for recommender systems , 2011, RecSys '11.

[22]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[23]  Ruth Herbert,et al.  Everyday Music Listening: Absorption, Dissociation and Trancing , 2011 .

[24]  Nuray Aykin,et al.  Individual differences in human-computer interaction: a survey , 1991 .

[25]  Barry Smyth,et al.  PeerChooser: visual interactive recommendation , 2008, CHI.

[26]  John Riedl,et al.  Tagsplanations: explaining recommendations using tags , 2009, IUI.

[27]  Michail Vlachos,et al.  Graph Embeddings for Movie Visualization and Recommendation , 2012 .

[28]  Katrien Verbert,et al.  How Do Different Levels of User Control Affect Cognitive Load and Acceptance of Recommendations? , 2017, IntRS@RecSys.

[29]  Katrien Verbert,et al.  Agents Vs. Users: Visual Recommendation of Research Talks with Multiple Dimension of Relevance , 2016, TIIS.

[30]  Björn Hartmann,et al.  The diversity donut: enabling participant control over the diversity of recommended responses , 2011, CHI EA '11.

[31]  Denis Parra,et al.  Moodplay: Interactive Mood-based Music Discovery and Recommendation , 2016, UMAP.

[32]  Bart P. Knijnenburg,et al.  Each to his own: how different users call for different interaction methods in recommender systems , 2011, RecSys '11.

[33]  Dorota Glowacka,et al.  Improving Controllability and Predictability of Interactive Recommendation Interfaces for Exploratory Search , 2015, IUI.

[34]  Peter Brusilovsky,et al.  User-controllable personalization: A case study with SetFusion , 2015, Int. J. Hum. Comput. Stud..

[35]  Peter Brusilovsky,et al.  User Models for Adaptive Hypermedia and Adaptive Educational Systems , 2007, The Adaptive Web.

[36]  Alessandro Micarelli,et al.  User Profiles for Personalized Information Access , 2007, The Adaptive Web.

[37]  Tobias Höllerer,et al.  SmallWorlds: Visualizing Social Recommendations , 2010, Comput. Graph. Forum.

[38]  Seyed Reza Shahamiri,et al.  A systematic review of scholar context-aware recommender systems , 2015, Expert Syst. Appl..

[39]  Katrien Verbert,et al.  Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities , 2016, Expert Syst. Appl..

[40]  Chaomei Chen,et al.  Visualizing the Semantic Web: XML-Based Internet and Information Visualization, 2nd Edition , 2004, Visualizing the Semantic Web, 2nd Edition.

[41]  Hisae Aoyama,et al.  Cognitive Analysis for Knowledge Modeling in Air Traffic Control Work , 2011, HCI.

[42]  Tobias Höllerer,et al.  Hypothetical Recommendation: A Study of Interactive Profile Manipulation Behavior for Recommender Systems , 2015, FLAIRS.

[43]  Erik Duval,et al.  Visualizing recommendations to support exploration, transparency and controllability , 2013, IUI '13.