Personality Traits Predict Music Taxonomy Preferences

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Impact of Listening Behavior on Music Recommendation.

The next generation of music recommendation systems willbe increasingly intelligent and likely take into account userbehavior for more personalized recommendations. In thiswork we consider user behavior when making recommendationswith features extracted from a user’s history of listeningevents. We investigate the impact of listener’s behaviorby considering features such as play counts, “mainstreaminess”,and diversity in music taste on the performanceof various music recommendation approaches. Theunderlying dataset has been collected by crawling socialmedia (specifically Twitter) for listening events. Each user’slistening behavior is characterized into a three dimensionalfeature space consisting of play count, “mainstreaminess”(i.e. the degree to which the observed user listens to currentlypopular artists), and diversity (i.e. the diversity ofgenres the observed user listens to). Drawing subsets ofthe 28,000 users in our dataset, according to these threedimensions, we evaluate whether these dimensions influencefigures of merit of various music recommendation approaches,in particular, collaborative filtering (CF) and CFenhanced by cultural information such as users located inthe same city or country

Algorithms Aside: Recommendation as the Lens of Life

In this position paper, we take the experimental approach of putting algorithms aside, and reflect on what recommenders would be for people if they were not tied to technology. By looking at some of the shortcomings that current recommenders have fallen into and discussing their limitations from a human point of view, we ask the question: if freed from all limitations, what should, and what could, RecSys be? We then turn to the idea that life itself is the best recommender system, and that people themselves are the query. By looking at how life brings people in contact with options that suit their needs or match their preferences, we hope to shed further light on what current RecSys could be doing better. Finally, we look at the forms that RecSys could take in the future. By formulating our vision beyond the reach of usual considerations and current limitations, including business models, algorithms, data sets, and evaluation methodologies, we attempt to arrive at fresh conclusions that may inspire the next steps taken by the community of researchers working on RecSys

You Are What You Post: What the Content of Instagram Pictures Tells About Users’ Personality

Instagram is a popular social networking application that allowsusers to express themselves through the uploaded contentand the different filters they can apply. In this study we look atthe relationship between the content of the uploaded Instagrampictures and the personality traits of users. To collect data, weconducted an online survey where we asked participants tofill in a personality questionnaire, and grant us access to theirInstagram account through the Instagram API. We gathered54,962 pictures of 193 Instagram users. Through the GoogleVision API, we analyzed the pictures on their content and clusteredthe returned labels with the k-means clustering approach.With a total of 17 clusters, we analyzed the relationship withusers’ personality traits. Our findings suggest a relationshipbetween personality traits and picture content. This allow fornew ways to extract personality traits from social media trails,and new ways to facilitate personalized systems

Eudaimonic Modeling of Moviegoers

One of the important aspects of movie-making is to trigger emotional responses in viewers. These emotional experiences can be divided into hedonic and eudaimonic. While the former are characterized as plain enjoyment, the latter deal with getting greater insight, self-reflection or contemplation. So far, modeling of user preferences about movies and personalization algorithms have largely ignored the eudaimonic aspect of the consumption of movies. In this paper we fill this gap by exploring what are the relationship between (i) eudaimonic and hedonic characteristics of movies, (ii) users' preferences and (iii) users' personality. Our results show that eudaimonic user profiling effectively divides users into pleasure-seekers and meaning-seekers

Theory-driven Recommendations : Modeling Hedonic and Eudaimonic Movie Preferences

Most of the research in recommender systems focuses on data-driven approaches. In this paper we present our vision for complementing data-driven approaches with model-driven ones. We present a preliminary experimental set-up and we expose our research plan. In the experimental set-up we acquired eudaimonic characteristics of movies and user preferences. Furthermore, we performed a preliminary analysis of the acquired data

The influence of personal values on music taste: Towards value-based music recommendations

The feld of recommender systems has a lot to gain from the feld of psychology. Indeed, many psychology researchers have investigated relations between models that describe humans and consumption preferences. One example of this is personality, which has been shown to be a valid construct to describe people. As a consequence, personality-based recommenders have already proven to be a lead toward improving recommendations, by adapting them to their users' traits. Beyond personality, there are more ways to describe a person's identity. One of these ways is to consider personal values: what is important for the users in life at the most abstract level. Being complementary to personality traits, values may give another lead towards better user understanding. In this paper, we investigate this, taking music as a use case. We use a marketing interview technique to elicit 22 users' personal values connected to their musical preferences. We show that personal values indeed play a role in people's music preferences, and are the frst to propose a map linking personal values to music preferences. We see this map as a frst step in devising a value-based user model for music recommender systems.Multimedia ComputingIntelligent System

Personality Traits and Music Genre Preferences : How Music Taste Varies Over Age Groups

Personality traits are increasingly being incorporated in systems to provide a personalized experience to the user. Current work focusing on identifying the relationship between personality and behavior, preferences, and needs often do not take into account differences between age groups. With music playing an important role in our lives, differences between age groups may be especially prevalent. In this work we investigate whether differences exist in music listening behavior between age groups. We analyzed a dataset with the music listening histories and personality information of 1415 users. Our results show agreements with prior work that identi\u80ed personality-based music listening preferences. However, our results show that the agreements we found are in some cases divided over different age groups, whereas in other cases additional correlations were found within age groups. With our results personality-based systems can provide better music recommendations that is in line with the user’s age

Personality-based active learning for collaborative filtering recommender systems

Abstract. Recommender systems (RSs) suffer from the cold-start or new user/item problem, i.e., the impossibility to provide a new user with accurate recommendations or to recommend new items. Active learning (AL) addresses this problem by actively selecting items to be presented to the user in order to acquire her ratings and hence improve the output of the RS. In this paper, we propose a novel AL approach that exploits the user’s personality- using the Five Factor Model (FFM)- in order to identify the items that the user is requested to rate. We have evaluated our approach in a user study by integrating it into a mobile, contextaware RS that provides users with recommendations for places of interest (POIs). We show that the proposed AL approach significantly increases the number of ratings acquired from the user and the recommendation accuracy.