This dataset is based on the LFM-1b [1] and the Cultural LFM-1b [2] datasets. LFM-BeyMS includes equally-sized groups of both, beyond-mainstream and mainstream music listeners and thus, can be used for studying the characteristics of beyond-mainstream music listeners for recommendation experiments. For more details, we refer to our publication in https://arxiv.org/abs/2102.12188. LFM-BeyMS contains * 4,148 users * 1,084,922 tracks * 110,898 artists * 16,687,363 listening events The python code utilized for generating and exhaustively analyzing this dataset can be found in https://github.com/pmuellner/supporttheunderground.
Music recommender systems have become central parts of popular streaming platforms such as this http URL, Pandora, or Spotify to help users find music that fits their preferences. These systems learn from the past listening events of users to recommend music a user will likely listen to in the future. Here, current algorithms typically employ collaborative filtering (CF) utilizing similarities between users' listening behaviors. Some approaches also combine CF with content features into hybrid recommender systems. While music recommender systems can provide quality recommendations to listeners of mainstream music artists, recent research has shown that they tend to discriminate listeners of unorthodox, low-mainstream artists. This is foremost due to the scarcity of usage data of low-mainstream music as music consumption patterns are biased towards popular artists. Thus, the objective of our work is to provide a novel approach for modeling artist preferences of users with different music consumption patterns and listening habits.
Classic resource recommenders like Collaborative Filtering (CF) treat users as being just another entity, neglecting non-linear user-resource dynamics shaping attention and interpretation. In this paper, we propose a novel hybrid recommendation strategy that refines CF by capturing these dynamics. The evaluation results reveal that our approach substantially improves CF and, depending on the dataset, successfully competes with a computationally much more expensive Matrix Factorization variant.
This is the dataset used in the study "Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes". The dataset is a subset of the LFM-1b LastFM dataset (http://www.cp.jku.at/datasets/LFM-1b/), which contains country-specific music listening events.
This dataset comprises a subset of rating data from five different datasets, i.e., Douban [1], Hetrec-MovieLens [2], MovieLens 1M [3], Ciao [4] and Jester [5]. Each subset represents rating data from three distinct user groups: users with few ratings (low), users with a medium amount of ratings (med) and users with lots of ratings (high). Each row in the user files includes a user's id and her number of ratings. The rows of the ratings files are in the format (user_id, item_id, rating). For more details, we refer to our publication in https://rd.springer.com/chapter/10.1007/978-3-030-72240-1_8. Douban * 375 users (i.e., 125 users per user group) * 32,191 items * 266,517 ratings Hetrec-MovieLens * 318 users (i.e., 106 users per user group) * 9,553 items * 207,943 ratings MovieLens 1M * 906 users (i.e., 302 users per user group) * 3,613 items * 275,119 ratings Ciao * 1,107 users (i.e., 369 users per user group) * 60,132 items * 107,807 ratings Jester * 11,013 users (i.e., 3,671 per user group) * 100 items * 618768 ratings The python code for generating and utilizing this dataset can be found in https://github.com/pmuellner/RobustnessOfMetaMF. This work is supported by the H2020 project TRUSTS (GA: 871481) and the "DDAI'' COMET Module within the COMET – Competence Centers for Excellent Technologies Programme, funded by the Austrian Federal Ministry for Transport, Innovation and Technology (bmvit), the Austrian Federal Ministry for Digital and Economic Affairs (bmdw), the Austrian Research Promotion Agency (FFG), the province of Styria (SFG) and partners from industry and academia. The COMET Programme is managed by FFG. [1] Hu, L., Sun, A., Liu, Y.: Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction. In: SIGIR’14 (2014) [2] Cantador, I., Brusilovsky, P., Kuflik, T.: Second international workshop on information heterogeneity and fusion in recommender systems (hetrec2011). In: RecSys’11(2011) [3] Harper, F. M., Konstan, J. A.: The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (TIIS) 5(4), 1–19 (2015) [4] Guo, G., Zhang, J., Thalmann, D., Yorke-Smith, N.: Etaf: An extended trust antecedents framework for trust prediction. In: ASONAM’14 (2014) [5] Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval 4(2), 133–151 (2001)
Recent research has unveiled the importance of online social networks for improving the quality of recommender systems and encouraged the research community to investigate better ways of exploiting the social information for recommendations. While most of the research focused on enhancing a traditional source of data (e.g., ratings, implicit feedback, or tags) with some type o f social information, little is known about how different sources of social data can be combined with other types of information relevant for recommendation. To contribute to this sparse field of resear ch, in this paper we exploit users’ interactions along three dimen sions of relevance (social, transactional, and location) to assess their performance in a barely studied domain: recommending items to people in an online marketplace environment. To that and we defined s ets of user similarity measures for each dimension of relevance and studied them isolated and in combination via hybrid recommender approaches, to assess which one provides the best recommendation performance. Interestingly, in our experiments conducted on a rich dataset collected from SecondLife, a popular online virtual world, we found that recommenders relying on similarity measures obtained from the social network yielded better results than those inferred directly from the marketplace data.
This is the dataset used in the study "Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes". The dataset is a subset of the LFM-1b LastFM dataset (http://www.cp.jku.at/datasets/LFM-1b/), which contains country-specific music listening events.
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