notesum.ai
Published at October 21STAR: A Simple Training-free Approach for Recommendations using Large Language Models
cs.LG
cs.AI
cs.RO
stat.ML
Released Date: October 21, 2024
Authors: Dong-Ho Lee1, Adam Kraft2, Long Jin3, Nikhil Mehta2, Taibai Xu3, Lichan Hong2, Ed H. Chi2, Xinyang Yi2
Aff.: 1University of Southern California; 2Google DeepMind; 3Google

| Category | Method / Model | Train | Beauty | Toys and Games | Sports and Outdoors | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| H@5 | N@5 | H@10 | N@10 | H@5 | N@5 | H@10 | N@10 | H@5 | N@5 | H@10 | N@10 | |||
| Baseline | KNN | ✓ | 0.004 | 0.003 | 0.007 | 0.004 | 0.004 | 0.003 | 0.007 | 0.004 | 0.001 | 0.001 | 0.002 | 0.001 |
| Caser (tang2018personalized, ) | ✓ | 0.021 | 0.013 | 0.035 | 0.018 | 0.017 | 0.011 | 0.027 | 0.014 | 0.012 | 0.007 | 0.019 | 0.010 | |
| HGN (ma2019hierarchical, ) | ✓ | 0.033 | 0.021 | 0.051 | 0.027 | 0.032 | 0.022 | 0.050 | 0.028 | 0.019 | 0.012 | 0.031 | 0.016 | |
| GRU4Rec (hidasi2015session, ) | ✓ | 0.016 | 0.010 | 0.028 | 0.014 | 0.010 | 0.006 | 0.018 | 0.008 | 0.013 | 0.009 | 0.020 | 0.011 | |
| BERT4Rec (sun2019bert4rec, ) | ✓ | 0.020 | 0.012 | 0.035 | 0.017 | 0.012 | 0.007 | 0.020 | 0.010 | 0.012 | 0.008 | 0.019 | 0.010 | |
| FDSA (zhang2019feature, ) | ✓ | 0.027 | 0.016 | 0.041 | 0.021 | 0.023 | 0.014 | 0.038 | 0.019 | 0.018 | 0.012 | 0.029 | 0.016 | |
| SASRec (kang2018self, ) | ✓ | 0.039 | 0.025 | 0.061 | 0.032 | 0.046 | 0.031 | 0.068 | 0.037 | 0.023 | 0.015 | 0.035 | 0.019 | |
| -Rec (zhou2020s3, ) | ✓ | 0.039 | 0.024 | 0.065 | 0.033 | 0.044 | 0.029 | 0.070 | 0.038 | 0.025 | 0.016 | 0.039 | 0.020 | |
| P5 (zhou2020s3, ) | ✓ | 0.016 | 0.011 | 0.025 | 0.014 | 0.007 | 0.005 | 0.012 | 0.007 | 0.006 | 0.004 | 0.010 | 0.005 | |
| TIGER (geng2022recommendation, ) | ✓ | 0.045 | 0.032 | 0.065 | 0.038 | 0.052 | 0.037 | 0.071 | 0.043 | 0.026 | 0.018 | 0.040 | 0.023 | |
| IDGenRec (tan2024idgenrec, ) | ✓ | 0.062 | 0.049 | 0.081 | 0.054 | 0.066 | 0.048 | 0.087 | 0.055 | 0.043 | 0.033 | 0.057 | 0.037 | |
| STAR-Retrieval | - | ✗ | 0.068 | 0.048 | 0.098 | 0.057 | 0.086 | 0.061 | 0.118 | 0.071 | 0.038 | 0.026 | 0.054 | 0.031 |
| STAR-Ranking | point-wise | ✗ | 0.068 | 0.047 | 0.096 | 0.056 | 0.086 | 0.061 | 0.117 | 0.071 | 0.037 | 0.026 | 0.054 | 0.031 |
| pair-wise | ✗ | 0.072 | 0.051 | 0.101 | 0.060 | 0.090 | 0.064 | 0.120 | 0.073 | 0.040 | 0.028 | 0.056 | 0.034 | |
| list-wise | ✗ | 0.065 | 0.047 | 0.090 | 0.055 | 0.083 | 0.060 | 0.111 | 0.069 | 0.036 | 0.026 | 0.052 | 0.031 | |