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
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Evalu
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Method
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)