14 research outputs found
On the Importance of Considering Country-specific Aspects on the Online-Market: An Example of Music Recommendation Considering Country-Specific Mainstream
In the field of music recommender systems, country-specific aspects have received little attention, although it is known that music perception and preferences are shaped by culture; and culture varies across countries. Based on the LFM-1b dataset (including 53,258 users from 47 countries), we show that there are significant country-specific differences in listenersâ music consumption behavior with respect to the most popular artists listened to. Results indicate that, for instance, Finnish usersâ listening behavior is farther away from the global mainstream, while United Statesâ listeners are close to the global mainstream. Relying on rating prediction experiments, we tailor recommendations to a userâs level of preference for mainstream (defined on a global level and on a country level) and the userâs country. Results suggest that, in terms of rating prediction accuracy, a combination of these two filtering strategies works particularly well for users of countries far away from the global mainstream
Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations
In this paper, we introduce a psychology-inspired approach to model and
predict the music genre preferences of different groups of users by utilizing
human memory processes. These processes describe how humans access information
units in their memory by considering the factors of (i) past usage frequency,
(ii) past usage recency, and (iii) the current context. Using a publicly
available dataset of more than a billion music listening records shared on the
music streaming platform Last.fm, we find that our approach provides
significantly better prediction accuracy results than various baseline
algorithms for all evaluated user groups, i.e., (i) low-mainstream music
listeners, (ii) medium-mainstream music listeners, and (iii) high-mainstream
music listeners. Furthermore, our approach is based on a simple psychological
model, which contributes to the transparency and explainability of the
calculated predictions.Comment: Dominik Kowald and Elisabeth Lex contributed equally to this wor
Investigating cross-country relationship between users' social ties and music mainstreaminess
We investigate the complex relationship between the fac- tors (i) preference for music mainstream, (ii) social ties in an online music platform, and (iii) demographics. We define (i) on a global and a country level, (ii) by several network centrality measures such as Jaccard index among usersâ connections, closeness centrality, and betweenness centrality, and (iii) by country and age information. Using the LFM-1b dataset of listening events of Last.fm users, we are able to uncover country-dependent differences in consumption of mainstream music as well as in user behavior with respect to social ties and usersâ centrality. We could identify that users inclined to mainstream music tend to have stronger connections than the group of less mainstreamy users. Furthermore, our analysis revealed that users typically have less connections within a country than cross-country ones, with the first being stronger social ties, though. Results will help building better user models of listeners and in turn improve personalized music retrieval and recommendation algorithms
Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship
Recommender systems have become the dominant means of curating cultural
content, significantly influencing the nature of individual cultural
experience. While the majority of research on recommender systems optimizes for
personalized user experience, this paradigm does not capture the ways that
recommender systems impact cultural experience in the aggregate, across
populations of users. Although existing novelty, diversity, and fairness
studies probe how systems relate to the broader social role of cultural
content, they do not adequately center culture as a core concept and challenge.
In this work, we introduce commonality as a new measure that reflects the
degree to which recommendations familiarize a given user population with
specified categories of cultural content. Our proposed commonality metric
responds to a set of arguments developed through an interdisciplinary dialogue
between researchers in computer science and the social sciences and humanities.
With reference to principles underpinning non-profit, public service media
systems in democratic societies, we identify universality of address and
content diversity in the service of strengthening cultural citizenship as
particularly relevant goals for recommender systems delivering cultural
content. Taking diversity in movie recommendation as a case study in enhancing
pluralistic cultural experience, we empirically compare systems' performance
using commonality and existing utility, diversity, and fairness metrics. Our
results demonstrate that commonality captures a property of system behavior
complementary to existing metrics and suggest the need for alternative,
non-personalized interventions in recommender systems oriented to strengthening
cultural citizenship across populations of users. In this way, commonality
contributes to a growing body of scholarship developing 'public good'
rationales for digital media and ML systems.Comment: The 16th ACM Conference on Recommender System