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Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning

NeurIPS 2019 · Reinforcement learning › RL from feedback · approach: Reward modeling versus direct optimization · 29 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add learned discriminator constraint (RCR)RL recommender · UT-Zappos50K unseenSR@10: 19% -> 74%Helped
add learned discriminator constraint (RCR)RL recommender · UT-Zappos50K unseenNV (violations): 70.02 ± 6.20 -> 11.32 ± 1.98Helped
add learned discriminator constraint (RCR)RL recommender · UT-Zappos50K unseenNI (interactions): 26.75 ± 1.67 -> 10.91 ± 1.06Helped
replace naive hard attribute constraints with learned discriminatorRL + Naive · UT-Zappos50K unseenSR@10: 52% -> 74%Helped
replace naive hard attribute constraints with learned discriminatorRL + Naive · UT-Zappos50K unseenSR@30: 94% -> 94%No clear effect
add naive hard constraintsRL recommender · UT-Zappos50K unseenSR@30: 63% -> 94%Helped
add learned discriminator constraint (RCR)RL text generator · Yelpviolation rate: 40.36% -> 10.49%Helped
add learned discriminator constraint (RCR)RL text generator · Yelptest-BLEU-2: 0.807 -> 0.840Helped
add learned discriminator constraint (RCR)RL text generator · Yelpself-BLEU-2: 0.658 -> 0.683Hurt

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