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U-NEED介绍

我们收集了一个以用户需求为中心的电商对话式推荐数据集(U-NEED)。

We collect a user needs-centric E-commerce conversational recommendation dataset (U-NEED).

U-NEED包含了7,698个细粒度标注的售前对话,333,879个用户行为和332,148条商品知识元组。

U-NEED consists of 7,698 fine-grained annotated pre-sales dialogues, 333,879 user behaviors and 332,148 product knowledge tuples.

对于售前对话的每一条语句,我们雇佣了专业的众包平台来标注:说话人的动作,语句涉及的属性和语句中推荐的商品。

For each utterance of pre-sales dialogue, we hire a professional crowdsourcing platform to annotate the action of the speaker, the attributes involved, and the recommended products.

基线模型结果 Baseline Results

任务一 Task 1

CategoryModelPrecisionRecallF1
AllBert+BiLSTM+CRF68.92%68.75%0.6884
Bert+CRF66.88%65.30%0.6608
Bert45.49%56.52%0.5041
BeautyBert+BiLSTM+CRF72.82%74.81%0.7380
Bert+CRF67.31%68.02%0.6766
Bert53.55%62.84%0.5782
FashionBert+BiLSTM+CRF65.89%70.71%0.6822
Bert+CRF60.16%66.61%0.6322
Bert46.45%58.39%0.5174
PhonesBert+BiLSTM+CRF67.01%69.90%0.6843
Bert+CRF56.20%59.23%0.5768
Bert42.12%53.84%0.4726
ElectronicBert+BiLSTM+CRF65.48%67.71%66.58%
Bert+CRF62.12%61.55%0.6183
Bert39.81%49.40%0.4409
ShoesBert+BiLSTM+CRF78.70%81.01%0.7984
Bert+CRF73.02%77.03%0.7497
Bert58.51%70.20%0.6382

任务二 Task 2

CategoryModelPrecisionRecallF1
AllDiaMultiClass0.32220.49660.3662
DiaSeq0.35550.29660.3153
BeautyDiaMultiClass0.40370.72280.3662
DiaSeq0.47610.42720.4424
FashionDiaMultiClass0.27110.34880.2918
DiaSeq0.15250.12710.1355
PhonesDiaMultiClass0.45340.52120.4585
DiaSeq0.44140.37890.3966
ElectronicDiaMultiClass0.25670.36570.2851
DiaSeq0.24200.17360.1891
ShoesDiaMultiClass0.33610.41310.3423
DiaSeq0.39920.33050.3498

任务三 Task 3

CategoryModelHits@10Hits@50NDCG@10NDCG@50MRR@10MRR@50
AllBert0.15930.3310.08180.11920.05820.066
SASRec0.140.27470.07250.10220.05220.0585
TGCRS0.1470.250.08090.10360.06060.0655
BeautyBert0.29850.39380.18420.20570.14840.1532
SASRec0.16310.31690.07260.10670.04490.0523
TGCRS0.28310.37230.17220.19140.13740.1413
FashionBert0.13480.14890.08540.08850.06970.0703
SASRec0.04960.08160.0260.03330.01880.0204
TGCRS0.10990.12410.07550.07860.0650.0657
PhonesBert0.42750.71740.24880.31380.19430.2086
SASRec0.40940.70650.21340.28080.15460.1698
TGCRS0.59420.76810.33920.37910.26090.2702
ElectronicBert0.25760.43330.14840.18640.11450.1222
SASRec0.17580.27880.10670.12830.08490.089
TGCRS0.28180.3970.1690.19490.13440.1402
ShoesBert0.10140.2550.04730.08130.03120.0384
SASRec0.06910.16740.03880.06020.02960.0341
TGCRS0.15210.2550.0830.10580.06180.0668

任务四 Task 4

CategoryModeldist@1dist@2dist@3dist@4bleu@1bleu@2bleu@3bleu@4InfoRel
AllGPT-20.02840.06240.17800.29050.06880.02760.01660.01360.57000.4267
Transformer0.014620.053660.15630.28060.11380.037150.020370.013591.15670.8800
KBRD0.011730.040610.12590.22330.12530.040670.025280.018791.13670.9167
NTRD0.14850.19420.22770.24890.04430.00820.00280.00161.00330.9900
BeautyGPT-20.05810.12500.25550.38110.06100.01760.00540.00250.64330.2767
Transformer0.036540.099770.25240.37140.094550.025160.014660.0098151.27670.5267
KBRD0.034660.087590.19770.28080.10970.03250.019990.013891.22330.6133
NTRD0.12590.22770.29630.32750.04390.00830.00420.00301.15000.6867
PhonesGPT-20.04970.10990.22040.32660.11100.04600.02480.01730.76330.4700
Transformer0.042540.12090.28530.40370.12790.043930.025460.014711.22670.9467
KBRD0.05060.13590.30170.41570.14180.043710.02040.0089481.09000.9867
NTRD0.16140.26660.31900.37750.05600.01630.01020.00721.04001.0567
ShoesGPT-20.05220.11710.2280.33570.08030.04050.02700.02020.50000.3767
Transformer0.03190.077720.19750.37180.1170.056290.040720.030351.04000.9600
KBRD0.035080.089010.22240.40510.13580.06850.047010.032191.09331.0467
NTRD0.22200.40360.44990.46350.04320.01350.00690.00411.01000.9600

任务五 Task 5

CategoryModelPCCSCCCos
AllDEB0.16170.18640.9212
P-value<6e-06<1e-07-
Bert-RUBER0.07420.10920.9214
P-value<0.0398<0.0024-
BeautyDEB0.16420.16280.9327
P-value<0.0299<0.0313-
Bert-RUBER0.09010.11330.9218
P-value-0.0126<0.0017-
PhonesDEB0.26780.28150.9366
P-value<0.0015<0.0008-
Bert-RUBER0.09000.11410.9218
P-value<0.0126<0.0015-
ShoesDEB0.15040.19630.9097
P-value<0.0416<0.0076-
Bert-RUBER0.09160.11570.9219
P-value<0.0111<0.0013-