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Top-N Recommendation with Multi-channel Positive Feedback using Factorization Machines

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A summary of our paper "Top-N Recommendation with Multi-Channel Positive Feedback using Factorization Machines", published at ACM TOIS and presented at DIR 2019 workshop.

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Top-N Recommendation with Multi-channel Positive Feedback using Factorization Machines

  1. 1. Top-N Recommendation with Multi-channel Positive Feedback with Factorization Machines Babak Loni, Roberto Pagano, Martha Larson, Alan Hanjalic DIR 2019, Amsterdam 29 Nov 2019 ACM Transactions On Information Systems (TOIS), Jan 2019
  2. 2. User feedback are drawn from different “channels”
  3. 3. • How can multiple types of feedback improve performance of recommendations? • Does channel-informed sampling allow for better exploitation of user feedback? • How do different sampling strategies perform on different datasets? Research Questions
  4. 4. - The items that are liked by the user is preferred by the items that user has not interacted with - Missing interactions are not necessarily considered as negative - Pairwise optimization Bayesian Personalized Ranking - General models, capable of learning latent factors for different features seamlessly Factorization Machines
  5. 5. Learning from multiple feedback types • Naïve modeling with FMs • Use type of feedback as auxiliary features • Adapted Sampling • Exploit type of feedback to adapt the sampling process
  6. 6. Learning from multiple feedback types • Adapted Sampling • Exploit type of feedback to adapt the sampling process Users have higher interest to items that they interact with stronger type of feedback
  7. 7. Adapted BPR Sampling • Triples are formed by sampling a positive feedback and a negative item Standard BPR Multi-Channel Sampling Non-UNI UNI / Non-UNI
  8. 8. - Uniform Item - Uniform Feedback - Items are sampled based on their popularity - Multi-channel - Items are sampled based on their level Sampling negative item - Uniform sampling - Multi-channel - Sampling depends on the level of feedback Sampling positive pair
  9. 9. Datasets Dataset #users #items #feedback Feedback types Kollekt.fm 15K 35K 168K XING 10K 10K 223K MovieLens 1M 6K 4K 1000K
  10. 10. Experiments Dataset Model Recall@10 Recall@20 NDCG@10 NDCG@20 Kollekt.fm BPR 0.1287 0.1807 0.0828 0.0989 FM-Pair (Multi-Channel) 0.1919 0.2747 0.1337 0.1598 XING BPR 0.1451 0.2342 0.1428 0.1765 FM-Pair (Multi-Channel) 0.2010 0.3188 0.1920 0.2365 ML1M BPR 0.1744 0.2740 0.3370 0.3357 FM-Pair (Multi-Channel) 0.1770 0.2770 0.3685 0.3505 BPR (with standard sampling) vs. FM-Pair with adapted multi-channel sampling. Note that best sampling strategy varies per dataset.
  11. 11. Experiments Accuracy, complexity and item coverage of different sampling strategies
  12. 12. Conclusions • Multi-Channel learning to rank can improve the accuracy of recommendations significantly • Exploiting the type of feedback via sampling outperforms the naïve integration of feedback type • The best sampling method typically depends on the properties of the dataset • Popularity oversampling improves the performance on popularity-skewed data
  13. 13. Thank you! http://babakx.github.io/WrapRec/About.html#tois2018 https://github.com/mkurovski/multi_channel_bpr Original Implementation in WrapRec (in C#) Python Implementation (Thanks to Marcel Kurovski) https://twitter.com/Babak_Loni

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