Bruce Ferwerda

Predicting Personality Traits with Instagram Pictures

Instagram is a popular social networking application, which allows photo-sharing and applying different photo filters to adjust the appearance of a picture. By applying photo filters, users are able to create a style that they want to express to their audience. In this study we tried to infer personality traits from the way users take pictures and apply filters to them. To investigate this relationship, we conducted an online survey where we asked participants to fill in a personality questionnaire, and grant us access to their Instagram account through the Instagram API. Among 113 participants and 22,398 extracted Instagram pictures, we found distinct picture features (e.g., hue, brightness, saturation) that are related to personality traits. Our findings suggest a relationship between personality traits and the way users want to make their pictures look. This allow for new ways to extract personality traits from social media trails, and new ways to facilitate personalized systems.

Using Instagram Picture Features to Predict Users' Personality

Instagram is a popular social networking application, which allows photo-sharing and applying different photo filters to adjust the appearance of a picture. By applying these filters, users are able to create a style that they want to express to their audience. In this study we tried to infer personality traits from the way users manipulate the appearance of their pictures by applying filters to them. To investigate this relationship, we studied the relationship between picture features and personality traits. To collect data, we conducted an online survey where we asked participants to fill in a personality questionnaire, and grant us access to their Instagram account through the Instagram API. Among 113 participants and 22,398 extracted Instagram pictures, we found distinct picture features e.g., relevant to hue, brightness, saturation that are related to personality traits. Our findings suggest a relationship between personality traits and these picture features. Based on our findings, we also show that personality traits can be accurately predicted. This allow for new ways to extract personality traits from social media trails, and new ways to facilitate personalized systems.

Personality Correlates for Digital Concert Program Notes

In classical music concerts, the concert program notes are distributed to the audience in order to provide background information on the composer, piece and performer. So far, these have been printed documents composed mostly of text. With some delay, mobile devices are making their way also in the world of classical concerts, hence offering additional options for digital program notes comprising not only text but also images, video and audio. Furthermore, these digital program notes can be personalized. In this paper, we present the results of a user study that relates personal characteristics (personality and background musical knowledge) to preferences for digital program notes.

Personality-Based User Modeling for Music Recommender Systems

Applications are getting increasingly interconnected. Al-though the interconnectedness provide new ways to gather information about the user, not all user information is ready to be directly implemented in order to provide a personalized experience to the user. Therefore, a general model is needed to which users’ behavior, preferences, and needs can be connected to. In this paper we present our works on a personality-based music recommender system in which we use users’ personality traits as a general model. We identified relationships between users’ personality and their behavior, preferences, and needs, and also investigated different ways to infer users’ personality traits from user-generated data of social networking sites (i.e., Facebook, Twitter, and Instagram). Our work contributes to new ways to mine and infer personality-based user models, and show how these models can be implemented in a music recommender system to positively contribute to the user experience.

Predicting Genre Preferences from Cultural and Socio-Economic Factors for Music Retrieval

In absence of individual user information, knowledge about larger user groups (e.g., country characteristics) can be exploited for deriving user preferences in order to provide recommendations to users. In this short paper, we study how to mitigate the cold-start problem on a country level for music retrieval. Specifically, we investigate a large-scale dataset on user listening behavior and show that we can reduce the error for predicting the popularity of genres in a country by about 16.4% over a baseline model using cultural and socio-economics indicators.

Personalizing Online Educational Tools

As more people turn to online resources to learn, there will be an increasing need for systems to understand and adapt to the needs of their users. Engagement is an important aspect to keep users committed to learning. Learning approaches for online systems can benefit from personalization to engage their users. However, many approaches for personalization currently rely on methods (e.g., historical behavioral data, questionnaires, quizzes) that are unable to provide a personalized experience from the start-of-use of a system. As users in a learning environment are exposed to new content, the first impression that they receive from the system influences their commitment with the program. In this position paper we propose a quantitative approach for personalization in online learning environments to overcome current problems for personalization in such environments.

Exploring Music Diversity Needs Across Countries

Providing diversity in recommendations has shown to positively influence the users subjective evaluations such as satisfaction. However, it is often unknown how much diversity a recommendation set needs to consist of. In this work, we explored how music users of Last.fm apply diversity in their listening behavior. We analyzed a dataset with the music listening history of 53,309 Last.fm users capturing their total listening events until August 2014. We complemented this dataset with The Echo Nest features and Hofstedes cultural dimensions to explore how music diversity is applied across countries. Between 47 countries, we found distinct relationships between the cultural dimensions and music diversity variables. These results suggest that different country-based diversity measurements should be considered when applied to a recommendation set in order to maximize the users subjective evaluations. The country-based relationships also provide opportunities for recommender systems to personalize experiences when user data is limited by being able to rely on the users demographics.

How item discovery enabled by diversity leads to increased recommendation list attractiveness

Applying diversity to a recommendation list has been shown to positively influence the user experience. A higher perceived diversity is argued to have a positive effect on the attractiveness of the recommendation list and a negative effect on the difficulty to make a choice. In a user study we presented 100 participants with several personalized lists of recommended music artists varying in levels of diversity. Participants were asked to assess these lists on perceived diversity and attractiveness, the experienced choice difficulty and discovery (i.e., the extent the list enriches their taste). We found that recommendation list attractiveness is influenced by two effects: 1) by diversity mediated through discovery; diverse recommendation lists are perceived to be more attractive if they enrich the users taste or 2) by the list familiarity; a higher list familiarity contributes to a higher list attractiveness. We additionally revealed how individual differences (i.e., familiarity) moderate the effects found. Our results have implications on the composition of diversified recommendation lists. Specifically recommended items should contribute in extending and/or deepening the users taste for the diversification to be effective.

Large-Scale Analysis of Group-Specific Music Genre Taste from Collaborative Tags

In this paper, we describe the LFM-1b User Genre Profile dataset. It provides detailed information on musical genre preferences for more than 120,000 listeners and links to the LFM-1b dataset. We created the dataset by exploiting social tags, indexing them using two genre term sets, and aggregating the resulting annotated listening events on the user level. We foresee several applications of the dataset in music retrieval and recommendation tasks, among others to build and evaluate decent user models, to alleviate cold-start situations in music recommender systems, and to increase their performance using the additional abstraction layer of genre. We further present results of statistical analyses of the dataset, regarding genre preferences and their consistencies. We do so for the entire user population and for user groups defined by demographic similarities. Moreover, we report interesting insights about correlations between musical preferences on the genre level.

IUI'17 Companion-Workshop Summary for HUMANIZE'17

The first workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE) took place in conjunction with the 22nd annual meeting of the intelligent user interfaces (IUI) community in Limassol, Cyprus on March 13, 2017. The goal of the workshop was to attract researchers from different fields by accepting contributions on the intersection of practical data mining methods and theoretical knowledge for personalization. A total of six papers were accepted for this edition of the workshop.