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Recsys2018 item recommendation on monotonic behavior chains

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Recsys2018論文読み会の資料

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Recsys2018 item recommendation on monotonic behavior chains

  1. 1. Recsys 2018 論文読み会 Item recommendation on monotonic behavior chains
  2. 2. Item recommendation on monotonic behavior chains Mengting Wan, Julian McAuley (University of California, San Diego) • Treat multiple types of feedback as monotonic behavior chains. • Multiple types of user feedback (e.g., click, purchase, review, recommend) • Several studies seek to connect implicit and explicit signals [8, 15, 18-20, survey 9] • Propose chainRec, which models multiple types of interactions as monotonic behavior chain. (Also releases new dataset, Goodreads, for this task.) • Factorization by CP/PARAFAC tensor decomposition. • Additive scoring function with a parametric rectifier • Edgewise optimization • Adding PMI to objectives for negative samples • Comparison of AUC and NDCG for Steam, Yoochoose, Yelp, GoogleLocal, Goodreads • Especially with variants or ablation model of proposed models. In Short Problem Related Work Contrib ution Evalu ation Method
  3. 3. Monotonic behavior chain and its tensor representation
  4. 4. Proposed Model parametric rectifier Monotonic scoring function CP/PARAFAC tensor decomposition
  5. 5. Optimization Adding PMI to objectives for negative samples (to adjust confidence for unobserved negatives) Edgewise optimization: training sampling focused on edges (edge = two consecutive stages where users exhibit different responses)
  6. 6. Other Optimization Optimize for each stage Optimize conditional probability
  7. 7. Dataset and Performance Comparison edgeOptsliceOptcondOpt
  8. 8. Additional Comparison Comparison on each stage Sensitivity to dimensionality
  9. 9. Visualization Separation of genres on each interaction’s embedding

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