This document summarizes a research paper on item recommendation using multiple types of user feedback modeled as monotonic behavior chains. It proposes a model called chainRec that uses tensor factorization and a parametric rectifier to learn embeddings for different interaction types. The model is optimized using edgewise optimization focusing on edges between consecutive interaction stages. The paper compares chainRec to ablation models on several datasets, finding it performs best, especially on recommending later interactions. Visualizations show it learns meaningful embeddings separating genres by interaction type.

1. Recsys 2018 論文読み会
Item recommendation on monotonic
behavior chains

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. Monotonic behavior chain and its tensor representation

4. Proposed Model
parametric rectifier
Monotonic scoring function
CP/PARAFAC tensor decomposition

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. Other Optimization
Optimize for each stage Optimize conditional
probability

7. Dataset and Performance Comparison
edgeOptsliceOptcondOpt

8. Additional Comparison
Comparison on
each stage
Sensitivity to
dimensionality

9. Visualization
Separation of genres on each interaction’s embedding