Fetching the paper…
Reading the bibliography…
With tremendous efforts on developing effective e-commerce models, conventional e-commerce models show limited success in generalist e-commerce modeling, and suffer from unsatisfactory performance on new users and new products - a typical out-of-domain generalization challenge.
Evaluation of entity resolution approaches on real-world match problems
Köpcke, H., Thor, A., and Rahm, E · 2010
Earlier work this paper cites.
Benchmark datasets for entity resolution
Rahm, Erhard · 2010
Earlier work this paper cites.
Facing the cold start problem in recommender systems
Lika, B., Kolomvatsos, K., and Hadjiefthymiades, S · 2013
Earlier work this paper cites.
A theoretical analysis of NDCG type ranking measures
Wang, Y., Wang, L., Li, Y., He, D., and Liu, T.-Y · 2013
Earlier work this paper cites.
Image-based recommendations on styles and substitutes
McAuley, J., Targett, C., Shi, Q., and Van Den Hengel, A · 2015
Earlier work this paper cites.
Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
He, R. and McAuley, J · 2016
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Modeling relational data with graph convolutional networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., Van Den Berg, R., Titov, I., and Welling, M · 2018
Earlier work this paper cites.
Unsupervised cross-lingual representation learning at scale
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., and Stoyanov, V · 2019
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
Earlier work this paper cites.
AmazonQA: A review-based question answering task
Gupta, M., Kulkarni, N., Chanda, R., Rayasam, A., and Lipton, Z. C · 2019
Earlier work this paper cites.
RoBERTa: A robustly optimized BERT pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
Earlier work this paper cites.
Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Ni, J., Li, J., and McAuley, J · 2019
Earlier work this paper cites.
Sentence-BERT: Sentence embeddings using siamese BERT-networks
Reimers, N. and Gurevych, I · 2019
Earlier work this paper cites.
Huggingface’s transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al · 2019
Earlier work this paper cites.
Scaling up open tagging from tens to thousands: Comprehension empowered attribute value extraction from product title
Xu, H., Wang, W., Mao, X., Jiang, X., and Lan, M · 2019
Earlier work this paper cites.
BERTScore: Evaluating text generation with BERT
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., and Artzi, Y · 2019
Earlier work this paper cites.
Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
Earlier work this paper cites.
BERTweet: A pre-trained language model for english tweets
Nguyen, D. Q., Vu, T., and Nguyen, A. G.-T · 2020
Earlier work this paper cites.
BLEURT: Learning robust metrics for text generation
Sellam, T., Das, D., and Parikh, A · 2020
Cited alongside, same era.
Learning to extract attribute value from product via question answering: A multi-task approach
Wang, Q., Yang, L., Kanagal, B., Sanghai, S., Sivakumar, D., Shu, B., Yu, Z., and Elsas, J · 2020
Cited alongside, same era.
Product knowledge graph embedding for e-commerce
Xu, D., Ruan, C., Korpeoglu, E., Kumar, S., and Achan, K · 2020
Cited alongside, same era.
A graph neural network approach for product relationship prediction
Ahmed, F., Cui, Y., Fu, Y., and Chen, W · 2021
Cited alongside, same era.
LoRA: Low-rank adaptation of large language models
Hu, E. J., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al · 2021
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization
Sanh, V., Webson, A., Raffel, C., Bach, S., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Raja, A., Dey, M., et al · 2021
Phi-2: The surprising power of small language models
Javaheripi, M. and Bubeck, S · 2023
Later among the works it cites.
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al · 2023
Later among the works it cites.
Platypus: Quick, cheap, and powerful refinement of LLMs
Lee, A., Hunter, C., and Ruiz, N · 2023
Later among the works it cites.
Text is all you need: Learning language representations for sequential recommendation
Li, J., Wang, M., Li, J., Fu, J., Shen, X., Shang, J., and McAuley, J · 2023
Later among the works it cites.
The Flan Collection: Designing data and methods for effective instruction tuning
Longpre, S., Hou, L., Vu, T., Webson, A., Chung, H. W., Tay, Y., Zhou, D., Le, Q. V., Zoph, B., Wei, J., and Roberts, A · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2021
Cited alongside, same era.
Zero-shot recommender systems
Ding, H., Deoras, A., Wang, B., and Wang, H · 2022
Cited alongside, same era.
Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (P5)
Geng, S., Liu, S., Fu, Z., Ge, Y., and Zhang, Y · 2022
Cited alongside, same era.
DeBERTaV3: Improving DeBERTa using electra-style pre-training with gradient-disentangled embedding sharing
He, P., Gao, J., and Chen, W · 2022
Cited alongside, same era.
Training compute-optimal large language models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. d. L., Hendricks, L. A., Welbl, J., Clark, A., et al · 2022
Cited alongside, same era.
Towards universal sequence representation learning for recommender systems
Hou, Y., Mu, S., Zhao, W. X., Li, Y., Ding, B., and Wen, J.-R · 2022
Cited alongside, same era.
OpenAI · 2023
Later among the works it cites.
gSASRec: Reducing overconfidence in sequential recommendation trained with negative sampling
Petrov, A. V. and Macdonald, C · 2023
Later among the works it cites.
LLaMA-E: Empowering e-commerce authoring with multi-aspect instruction following
Shi, K., Sun, X., Wang, D., Fu, Y., Xu, G., and Li, Q · 2023
Later among the works it cites.
Comparing traditional and LLM-based search for consumer choice: A randomized experiment
Spatharioti, S. E., Rothschild, D. M., Goldstein, D. G., and Hofman, J. M · 2023
Later among the works it cites.
Gemini: a family of highly capable multimodal models
Team, G., Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., et al · 2023
Later among the works it cites.
RecMind: Large language model powered agent for recommendation
Wang, Y., Jiang, Z., Chen, Z., Yang, F., Zhou, Y., Cho, E., Fan, X., Huang, X., Lu, Y., and Yang, Y · 2023
Later among the works it cites.
Large language model can interpret latent space of sequential recommender
Yang, Z., Wu, J., Luo, Y., Zhang, J., Yuan, Y., Zhang, A., Wang, X., and He, X · 2023
Later among the works it cites.
Large language models for robotics: A survey
Zeng, F., Gan, W., Wang, Y., Liu, N., and Yu, P. S · 2023
Later among the works it cites.
A survey of large language models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al · 2023
Later among the works it cites.
Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al · 2024
Closest in time.
Datacomp: In search of the next generation of multimodal datasets
Gadre, S. Y., Ilharco, G., Fang, A., Hayase, J., Smyrnis, G., Nguyen, T., Marten, R., Wortsman, M., Ghosh, D., Zhang, J., et al · 2024
Closest in time.
Large language models are zero-shot rankers for recommender systems
Hou, Y., Zhang, J., Lin, Z., Lu, H., Xie, R., McAuley, J., and Zhao, W. X · 2024
Closest in time.
Ecomgpt: Instruction-tuning large language models with chain-of-task tasks for e-commerce
Li, Y., Ma, S., Wang, X., Huang, S., Jiang, C., Zheng, H.-T., Xie, P., Huang, F., and Jiang, Y · 2024
Closest in time.
MAmmoTH: Building math generalist models through hybrid instruction tuning
Yue, X., Qu, X., Zhang, G., Fu, Y., Huang, W., Sun, H., Su, Y., and Chen, W · 2024
Closest in time.