Two-stage Model for Automatic Playlist Continuation at Scale

@article{Volkovs2018TwostageMF,
  title={Two-stage Model for Automatic Playlist Continuation at Scale},
  author={Maksims Volkovs and Himanshu Rai and Zhaoyue Cheng and Ga Wu and Yichao Lu and Scott Sanner},
  journal={Proceedings of the ACM Recommender Systems Challenge 2018},
  year={2018}
}
Automatic playlist continuation is a prominent problem in music recommendation. Significant portion of music consumption is now done online through playlists and playlist-like online radio stations. Manually compiling playlists for consumers is a highly time consuming task that is difficult to do at scale given the diversity of tastes and the large amount of musical content available. Consequently, automated playlist continuation has received increasing attention recently [1, 7, 11]. The 2018… Expand
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An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist Continuation
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The ACM Recommender Systems Challenge 2018 focused on the task of automatic music playlist continuation, which is a form of the more general task of sequential recommendation, to recommend up to 500 tracks that fit the target characteristics of the original playlist. Expand
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This work formulate the problem of learning a fixed-length playlist representation in an unsupervised manner, using Sequence-to-sequence (Seq2seq) models, interpreting playlists as sentences and songs as words, and compares the model with two other encoding architectures for baseline comparison. Expand
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  • Computer Science, Engineering
  • 2019 International Joint Conference on Neural Networks (IJCNN)
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The results improve upon baseline works, verify the influence of the producer effect on classification performance and demonstrate the trade-offs between audio length and training set size. Expand
Evaluating Recommender System Algorithms for Generating Local Music Playlists
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This paper alters the standard evaluation procedure such that the algorithms only rank tracks by local artists for each of the eight different cities, and finds that the neighborhood-based approach (IIN) performs best for long-tail local music recommendation. Expand
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A novel user modeling approach, BLLu, which takes into account the popularity of music genres as well as temporal drifts of user listening behavior and adopts a psychological model that describes how humans access information in their memory is introduced. Expand
Consistency-Aware Recommendation for User-Generated Item List Continuation
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A generalizable approach that models the consistency of item lists based on human curation patterns, and so can be deployed across a wide range of varying item types, and can dynamically adapt as user preferences evolve. Expand
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This paper investigates possible reasons for the almost consistent success of DL based models in recommendation-related machine learning competitions and considers multiple possible factors such as the characteristics and complexity of the problem settings, datasets, and DL methods. Expand
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The approach to this problem and the final system that was submitted to the challenge are described, which consists in combining the predictions of multiple boosting trees models trained with features extracted from the sessions and the tracks. Expand
Noise Contrastive Estimation for One-Class Collaborative Filtering
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NCE item embeddings combined with a personalized user model from PLRec produces superior recommendations that adequately account for popularity bias, and analysis of the popularity distribution of recommended items demonstrates that NCE-PLRec uniformly distributes recommendations over the popularity spectrum while other methods exhibit distinct biases towards specific popularity subranges. Expand
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The ACM Recommender Systems Challenge 2018 focused on automatic music playlist continuation, which is a form of the more general task of sequential recommendation, to recommend up to 500 tracks that fit the target characteristics of the original playlist. Expand
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