Built on

He, R., McAuley, J.: Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th International Conference on World Wide Web, pp. 507–517 (2016)
2016
Earlier work this paper cites.
Wu, Y., Wu, W., Xing, C., Zhou, M., Li, Z.: Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 496–505 (2017)
2017
Earlier work this paper cites.
Wan, M., McAuley, J.: Item recommendation on monotonic behavior chains. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 86–94 (2018)
2018
Earlier work this paper cites.
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT (1), pp. 4171–4186 (2019)
2019
Earlier work this paper cites.
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., Jiang, P.: Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. In: CIKM, pp. 1441–1450 (2019)
2019
Earlier work this paper cites.
Da’u, A., Salim, N.: Recommendation system based on deep learning methods: a systematic review and new directions. Artificial Intelligence Review 53
2020
Earlier work this paper cites.
Penha, G., Hauff, C.: What does BERT know about books, movies and music? probing BERT for conversational recommendation. In: RecSys, pp. 388–397 (2020)
2020
Earlier work this paper cites.
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., Amodei, D.: Language models are few-shot learners. In: NeurIPS (2020)
2020
Earlier work this paper cites.
Wu, F., Qiao, Y., Chen, J.-H., Wu, C., Qi, T., Lian, J., Liu, D., Xie, X., Gao, J., Wu, W., et al
2020
Earlier work this paper cites.
Zeng, Z., Xiao, C., Yao, Y., Xie, R., Liu, Z., Lin, F., Lin, L., Sun, M.: Knowledge transfer via pre-training for recommendation: A review and prospect. Frontiers in big Data 4
2021
Earlier work this paper cites.
Qiu, Z., Wu, X., Gao, J., Fan, W.: U-BERT: pre-training user representations for improved recommendation. In: AAAI, pp. 4320–4327 (2021)
2021
Earlier work this paper cites.
Wu, C., Wu, F., Yu, Y., Qi, T., Huang, Y., Xie, X.: Userbert: Contrastive user model pre-training. arXiv preprint arXiv:2109.01274 (2021)
Original
2021
Earlier work this paper cites.
Muhamed, A., Keivanloo, I., Perera, S., Mracek, J., Xu, Y., Cui, Q., Rajagopalan, S., Zeng, B., Chilimbi, T.: Ctr-bert: Cost-effective knowledge distillation for billion-parameter teacher models. In: NeurIPS Efficient Natural Language and Speech Processing Workshop (2021)
2021
Earlier work this paper cites.
Wu, C., Wu, F., Qi, T., Huang, Y.: Empowering news recommendation with pre-trained language models. In: SIGIR, pp. 1652–1656 (2021)
2021
Earlier work this paper cites.
Lester, B., Al-Rfou, R., Constant, N.: The power of scale for parameter-efficient prompt tuning. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 3045–3059 (2021)
2021
Earlier work this paper cites.
Geng, S., Liu, S., Fu, Z., Ge, Y., Zhang, Y.: Recommendation as language processing (RLP): A unified pretrain, personalized prompt & predict paradigm (P5). In: RecSys, pp. 299–315 (2022)
2022
Earlier work this paper cites.
Sileo, D., Vossen, W., Raymaekers, R.: Zero-shot recommendation as language modeling. In: ECIR (2). Lecture Notes in Computer Science, vol. 13186, pp. 223–230 (2022)
2022
Earlier work this paper cites.
Yang, Y., Qiao, Y., Shao, J., Yan, X., Yang, T.: Lightweight composite re-ranking for efficient keyword search with BERT. In: WSDM, pp. 1234–1244 (2022)
2022
Earlier work this paper cites.
Wu, X., Magnani, A., Chaidaroon, S., Puthenputhussery, A., Liao, C., Fang, Y.: A multi-task learning framework for product ranking with BERT. In: WWW, pp. 493–501 (2022)
2022
Earlier work this paper cites.
Zhang, S., Zheng, N., Wang, D.: GBERT: pre-training user representations for ephemeral group recommendation. In: CIKM, pp. 2631–2639 (2022)
2022
Earlier work this paper cites.
Yao, S., Tan, J., Chen, X., Zhang, J., Zeng, X., Yang, K.: Reprbert: Distilling BERT to an efficient representation-based relevance model for e-commerce. In: KDD, pp. 4363–4371 (2022)
2022
Earlier work this paper cites.
Zhao, Q.: Resetbert4rec: A pre-training model integrating time and user historical behavior for sequential recommendation. In: SIGIR, pp. 1812–1816 (2022)
2022
Earlier work this paper cites.
Hou, Y., Mu, S., Zhao, W.X., Li, Y., Ding, B., Wen, J.: Towards universal sequence representation learning for recommender systems. In: KDD, pp. 585–593 (2022)
2022
Earlier work this paper cites.
Yu, Y., Wu, F., Wu, C., Yi, J., Liu, Q.: Tiny-newsrec: Effective and efficient plm-based news recommendation. In: EMNLP, pp. 5478–5489 (2022)
2022
Earlier work this paper cites.
Liu, Q., Zhu, J., Dai, Q., Wu, X.: Boosting deep CTR prediction with a plug-and-play pre-trainer for news recommendation. In: COLING, pp. 2823–2833 (2022)
2022
Earlier work this paper cites.
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X.V., et al.: Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068 (2022)
Original
2022
Earlier work this paper cites.
Yang, B., Han, C., Li, Y., Zuo, L., Yu, Z.: Improving conversational recommendation systems’ quality with context-aware item meta-information. In: Findings of the Association for Computational Linguistics: NAACL 2022, pp. 38–48 (2022)
2022
Earlier work this paper cites.
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C.L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P.F., Leike, J., Lowe, R.: Training language models to follow instructions with human feedback. In: NeurIPS (2022)
2022
Earlier work this paper cites.
Dai, D., Sun, Y., Dong, L., Hao, Y., Sui, Z., Wei, F.: Why can GPT learn in-context? language models secretly perform gradient descent as meta-optimizers. CoRR abs/2212.10559
Original
2022
Earlier work this paper cites.
Wang, X., Zhou, K., Wen, J., Zhao, W.X.: Towards unified conversational recommender systems via knowledge-enhanced prompt learning. In: KDD, pp. 1929–1937 (2022)
2022
Earlier work this paper cites.
Cui, Z., Ma, J., Zhou, C., Zhou, J., Yang, H.: M6-rec: Generative pretrained language models are open-ended recommender systems. CoRR abs/2205.08084
Original
2022
Earlier work this paper cites.
Xiao, S., Liu, Z., Shao, Y., Di, T., Middha, B., Wu, F., Xie, X.: Training large-scale news recommenders with pretrained language models in the loop. In: KDD, pp. 4215–4225 (2022)
2022
Earlier work this paper cites.
Yuan, G., Yuan, F., Li, Y., Kong, B., Li, S., Chen, L., Yang, M., Yu, C., Hu, B., Li, Z., et al.: Tenrec: A large-scale multipurpose benchmark dataset for recommender systems. arXiv preprint arXiv:2210.10629 (2022)
Original
2022
Earlier work this paper cites.
Lu, Y., Bartolo, M., Moore, A., Riedel, S., Stenetorp, P.: Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 8086–8098 (2022)
2022
Earlier work this paper cites.
Jang, J., Ye, S., Yang, S., Shin, J., Han, J., Kim, G., Choi, S.J., Seo, M.: Towards continual knowledge learning of language models. In: ICLR (2022)
2022
Earlier work this paper cites.
Liu, P., Zhang, L., Gulla, J.A.: Pre-train, prompt and recommendation: A comprehensive survey of language modelling paradigm adaptations in recommender systems. arXiv preprint arXiv:2302.03735 (2023)
Original
2023
Earlier work this paper cites.
Zhao, W.X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al.: A survey of large language models. arXiv preprint arXiv:2303.18223 (2023)
Original
2023
Earlier work this paper cites.
Gao, Y., Sheng, T., Xiang, Y., Xiong, Y., Wang, H., Zhang, J.: Chat-rec: Towards interactive and explainable llms-augmented recommender system. CoRR abs/2303.14524
Original
2023
Earlier work this paper cites.
Li, L., Zhang, Y., Chen, L.: Personalized prompt learning for explainable recommendation. ACM Transactions on Information Systems 41
2023
Earlier work this paper cites.
Chen, J., Liu, Z., Huang, X., Wu, C., Liu, Q., Jiang, G., Pu, Y., Lei, Y., Chen, X., Wang, X., Lian, D., Chen, E.: When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities (2023)
2023
Earlier work this paper cites.
Rahmani, S., Naghshzan, A., Guerrouj, L.: Improving code example recommendations on informal documentation using bert and query-aware lsh: A comparative study. arXiv preprint arXiv:2305.03017 (2023)
Original
2023
Earlier work this paper cites.
Fu, J., Yuan, F., Song, Y., Yuan, Z., Cheng, M., Cheng, S., Zhang, J., Wang, J., Pan, Y.: Exploring adapter-based transfer learning for recommender systems: Empirical studies and practical insights. arXiv preprint arXiv:2305.15036 (2023)
Original
2023
Earlier work this paper cites.
Hou, Y., He, Z., McAuley, J., Zhao, W.X.: Learning vector-quantized item representation for transferable sequential recommenders. In: Proceedings of the ACM Web Conference 2023. WWW ’23, pp. 1162–1171, New York, NY, USA (2023)
2023
Earlier work this paper cites.

Similar

Yuan, Z., Yuan, F., Song, Y., Li, Y., Fu, J., Yang, F., Pan, Y., Ni, Y.: Where to go next for recommender systems? id-vs. modality-based recommender models revisited. arXiv preprint arXiv:2303.13835 (2023)
Original
2023
Cited alongside, same era.
Li, R., Deng, W., Cheng, Y., Yuan, Z., Zhang, J., Yuan, F.: Exploring the upper limits of text-based collaborative filtering using large language models: Discoveries and insights. arXiv preprint arXiv:2305.11700 (2023)
Original
2023
Cited alongside, same era.
Shen, T., Li, J., Bouadjenek, M.R., Mai, Z., Sanner, S.: Towards understanding and mitigating unintended biases in language model-driven conversational recommendation. Information Processing & Management 60
2023
Cited alongside, same era.
Zhang, Z., Wang, B.: Prompt learning for news recommendation. arXiv preprint arXiv:2304.05263 (2023)
Original
2023
Cited alongside, same era.

Then

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alphaXiv searches the wider corpus for related work and actual follow-ups.

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2023

A Survey on Large Language Models for Recommendation

Wu, Likang, Zheng, Zhi, Qiu, Zhaopeng et al.

Understand

Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS).

These models, trained on massive amounts of data using self-supervised learning, have demonstrated remarkable success in learning universal representations and have the potential to enhance various aspects of recommendation systems by some effective transfer techniques such as fine-tuning and prompt tuning, and so on.
The crucial aspect of harnessing the power of language models in enhancing recommendation quality is the utilization of their high-quality representations of textual features and their extensive coverage of external knowledge to establish correlations between items and users.
To provide a comprehensive understanding of the existing LLM-based recommendation systems, this survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec), with the latter being systematically sorted out for the first time.

Built on

He, R., McAuley, J.: Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th International Conference on World Wide Web, pp. 507–517 (2016)
2016
Earlier work this paper cites.
Wu, Y., Wu, W., Xing, C., Zhou, M., Li, Z.: Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 496–505 (2017)
2017
Earlier work this paper cites.
Wan, M., McAuley, J.: Item recommendation on monotonic behavior chains. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 86–94 (2018)
2018
Earlier work this paper cites.
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT (1), pp. 4171–4186 (2019)
2019
Earlier work this paper cites.
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., Jiang, P.: Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. In: CIKM, pp. 1441–1450 (2019)
2019
Earlier work this paper cites.
Da’u, A., Salim, N.: Recommendation system based on deep learning methods: a systematic review and new directions. Artificial Intelligence Review 53
2020
Earlier work this paper cites.
Penha, G., Hauff, C.: What does BERT know about books, movies and music? probing BERT for conversational recommendation. In: RecSys, pp. 388–397 (2020)
2020
Earlier work this paper cites.
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., Amodei, D.: Language models are few-shot learners. In: NeurIPS (2020)
2020
Earlier work this paper cites.
Wu, F., Qiao, Y., Chen, J.-H., Wu, C., Qi, T., Lian, J., Liu, D., Xie, X., Gao, J., Wu, W., et al
2020
Earlier work this paper cites.
Zeng, Z., Xiao, C., Yao, Y., Xie, R., Liu, Z., Lin, F., Lin, L., Sun, M.: Knowledge transfer via pre-training for recommendation: A review and prospect. Frontiers in big Data 4
2021
Earlier work this paper cites.
Qiu, Z., Wu, X., Gao, J., Fan, W.: U-BERT: pre-training user representations for improved recommendation. In: AAAI, pp. 4320–4327 (2021)
2021
Earlier work this paper cites.
Wu, C., Wu, F., Yu, Y., Qi, T., Huang, Y., Xie, X.: Userbert: Contrastive user model pre-training. arXiv preprint arXiv:2109.01274 (2021)
Original
2021
Earlier work this paper cites.
Muhamed, A., Keivanloo, I., Perera, S., Mracek, J., Xu, Y., Cui, Q., Rajagopalan, S., Zeng, B., Chilimbi, T.: Ctr-bert: Cost-effective knowledge distillation for billion-parameter teacher models. In: NeurIPS Efficient Natural Language and Speech Processing Workshop (2021)
2021
Earlier work this paper cites.
Wu, C., Wu, F., Qi, T., Huang, Y.: Empowering news recommendation with pre-trained language models. In: SIGIR, pp. 1652–1656 (2021)
2021
Earlier work this paper cites.
Lester, B., Al-Rfou, R., Constant, N.: The power of scale for parameter-efficient prompt tuning. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 3045–3059 (2021)
2021
Earlier work this paper cites.
Geng, S., Liu, S., Fu, Z., Ge, Y., Zhang, Y.: Recommendation as language processing (RLP): A unified pretrain, personalized prompt & predict paradigm (P5). In: RecSys, pp. 299–315 (2022)
2022
Earlier work this paper cites.
Sileo, D., Vossen, W., Raymaekers, R.: Zero-shot recommendation as language modeling. In: ECIR (2). Lecture Notes in Computer Science, vol. 13186, pp. 223–230 (2022)
2022
Earlier work this paper cites.
Yang, Y., Qiao, Y., Shao, J., Yan, X., Yang, T.: Lightweight composite re-ranking for efficient keyword search with BERT. In: WSDM, pp. 1234–1244 (2022)
2022
Earlier work this paper cites.
Wu, X., Magnani, A., Chaidaroon, S., Puthenputhussery, A., Liao, C., Fang, Y.: A multi-task learning framework for product ranking with BERT. In: WWW, pp. 493–501 (2022)
2022
Earlier work this paper cites.
Zhang, S., Zheng, N., Wang, D.: GBERT: pre-training user representations for ephemeral group recommendation. In: CIKM, pp. 2631–2639 (2022)
2022
Earlier work this paper cites.
Yao, S., Tan, J., Chen, X., Zhang, J., Zeng, X., Yang, K.: Reprbert: Distilling BERT to an efficient representation-based relevance model for e-commerce. In: KDD, pp. 4363–4371 (2022)
2022
Earlier work this paper cites.
Zhao, Q.: Resetbert4rec: A pre-training model integrating time and user historical behavior for sequential recommendation. In: SIGIR, pp. 1812–1816 (2022)
2022
Earlier work this paper cites.
Hou, Y., Mu, S., Zhao, W.X., Li, Y., Ding, B., Wen, J.: Towards universal sequence representation learning for recommender systems. In: KDD, pp. 585–593 (2022)
2022
Earlier work this paper cites.
Yu, Y., Wu, F., Wu, C., Yi, J., Liu, Q.: Tiny-newsrec: Effective and efficient plm-based news recommendation. In: EMNLP, pp. 5478–5489 (2022)
2022
Earlier work this paper cites.
Liu, Q., Zhu, J., Dai, Q., Wu, X.: Boosting deep CTR prediction with a plug-and-play pre-trainer for news recommendation. In: COLING, pp. 2823–2833 (2022)
2022
Earlier work this paper cites.
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X.V., et al.: Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068 (2022)
Original
2022
Earlier work this paper cites.
Yang, B., Han, C., Li, Y., Zuo, L., Yu, Z.: Improving conversational recommendation systems’ quality with context-aware item meta-information. In: Findings of the Association for Computational Linguistics: NAACL 2022, pp. 38–48 (2022)
2022
Earlier work this paper cites.
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C.L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P.F., Leike, J., Lowe, R.: Training language models to follow instructions with human feedback. In: NeurIPS (2022)
2022
Earlier work this paper cites.
Dai, D., Sun, Y., Dong, L., Hao, Y., Sui, Z., Wei, F.: Why can GPT learn in-context? language models secretly perform gradient descent as meta-optimizers. CoRR abs/2212.10559
Original
2022
Earlier work this paper cites.
Wang, X., Zhou, K., Wen, J., Zhao, W.X.: Towards unified conversational recommender systems via knowledge-enhanced prompt learning. In: KDD, pp. 1929–1937 (2022)
2022
Earlier work this paper cites.
Cui, Z., Ma, J., Zhou, C., Zhou, J., Yang, H.: M6-rec: Generative pretrained language models are open-ended recommender systems. CoRR abs/2205.08084
Original
2022
Earlier work this paper cites.
Xiao, S., Liu, Z., Shao, Y., Di, T., Middha, B., Wu, F., Xie, X.: Training large-scale news recommenders with pretrained language models in the loop. In: KDD, pp. 4215–4225 (2022)
2022
Earlier work this paper cites.
Yuan, G., Yuan, F., Li, Y., Kong, B., Li, S., Chen, L., Yang, M., Yu, C., Hu, B., Li, Z., et al.: Tenrec: A large-scale multipurpose benchmark dataset for recommender systems. arXiv preprint arXiv:2210.10629 (2022)
Original
2022
Earlier work this paper cites.
Lu, Y., Bartolo, M., Moore, A., Riedel, S., Stenetorp, P.: Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 8086–8098 (2022)
2022
Earlier work this paper cites.
Jang, J., Ye, S., Yang, S., Shin, J., Han, J., Kim, G., Choi, S.J., Seo, M.: Towards continual knowledge learning of language models. In: ICLR (2022)
2022
Earlier work this paper cites.
Liu, P., Zhang, L., Gulla, J.A.: Pre-train, prompt and recommendation: A comprehensive survey of language modelling paradigm adaptations in recommender systems. arXiv preprint arXiv:2302.03735 (2023)
Original
2023
Earlier work this paper cites.
Zhao, W.X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al.: A survey of large language models. arXiv preprint arXiv:2303.18223 (2023)
Original
2023
Earlier work this paper cites.
Gao, Y., Sheng, T., Xiang, Y., Xiong, Y., Wang, H., Zhang, J.: Chat-rec: Towards interactive and explainable llms-augmented recommender system. CoRR abs/2303.14524
Original
2023
Earlier work this paper cites.
Li, L., Zhang, Y., Chen, L.: Personalized prompt learning for explainable recommendation. ACM Transactions on Information Systems 41
2023
Earlier work this paper cites.
Chen, J., Liu, Z., Huang, X., Wu, C., Liu, Q., Jiang, G., Pu, Y., Lei, Y., Chen, X., Wang, X., Lian, D., Chen, E.: When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities (2023)
2023
Earlier work this paper cites.
Rahmani, S., Naghshzan, A., Guerrouj, L.: Improving code example recommendations on informal documentation using bert and query-aware lsh: A comparative study. arXiv preprint arXiv:2305.03017 (2023)
Original
2023
Earlier work this paper cites.
Fu, J., Yuan, F., Song, Y., Yuan, Z., Cheng, M., Cheng, S., Zhang, J., Wang, J., Pan, Y.: Exploring adapter-based transfer learning for recommender systems: Empirical studies and practical insights. arXiv preprint arXiv:2305.15036 (2023)
Original
2023
Earlier work this paper cites.
Hou, Y., He, Z., McAuley, J., Zhao, W.X.: Learning vector-quantized item representation for transferable sequential recommenders. In: Proceedings of the ACM Web Conference 2023. WWW ’23, pp. 1162–1171, New York, NY, USA (2023)
2023
Earlier work this paper cites.

Similar

Yuan, Z., Yuan, F., Song, Y., Li, Y., Fu, J., Yang, F., Pan, Y., Ni, Y.: Where to go next for recommender systems? id-vs. modality-based recommender models revisited. arXiv preprint arXiv:2303.13835 (2023)
Original
2023
Cited alongside, same era.
Li, R., Deng, W., Cheng, Y., Yuan, Z., Zhang, J., Yuan, F.: Exploring the upper limits of text-based collaborative filtering using large language models: Discoveries and insights. arXiv preprint arXiv:2305.11700 (2023)
Original
2023
Cited alongside, same era.
Shen, T., Li, J., Bouadjenek, M.R., Mai, Z., Sanner, S.: Towards understanding and mitigating unintended biases in language model-driven conversational recommendation. Information Processing & Management 60
2023
Cited alongside, same era.
Zhang, Z., Wang, B.: Prompt learning for news recommendation. arXiv preprint arXiv:2304.05263 (2023)
Original
2023
Cited alongside, same era.

Then

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Liu, J., Liu, C., Lv, R., Zhou, K., Zhang, Y.: Is chatgpt a good recommender? A preliminary study. CoRR abs/2304.10149

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2023

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Dai, S., Shao, N., Zhao, H., Yu, W., Si, Z., Xu, C., Sun, Z., Zhang, X., Xu, J.: Uncovering chatgpt’s capabilities in recommender systems. CoRR abs/2305.02182

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2023

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Lin, G., Zhang, Y.: Sparks of artificial general recommender (AGR): early experiments with chatgpt. CoRR abs/2305.04518

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Sanner, S., Balog, K., Radlinski, F., Wedin, B., Dixon, L.: Large language models are competitive near cold-start recommenders for language-and item-based preferences. In: Proceedings of the 17th ACM Conference on Recommender Systems, pp. 890–896 (2023)

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Cited alongside, same era.

Mysore, S., McCallum, A., Zamani, H.: Large language model augmented narrative driven recommendations. arXiv preprint arXiv:2306.02250 (2023)

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Xi, Y., Liu, W., Lin, J., Zhu, J., Chen, B., Tang, R., Zhang, W., Zhang, R., Yu, Y.: Towards open-world recommendation with knowledge augmentation from large language models. ArXiv abs/2306.10933

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2023

Cited alongside, same era.

Hou, Y., Zhang, J., Lin, Z., Lu, H., Xie, R., McAuley, J.J., Zhao, W.X.: Large language models are zero-shot rankers for recommender systems. CoRR abs/2305.08845

Original

2023

Cited alongside, same era.

Sun, W., Yan, L., Ma, X., Ren, P., Yin, D., Ren, Z.: Is chatgpt good at search? investigating large language models as re-ranking agent. CoRR abs/2304.09542

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Cited alongside, same era.

Wu, L., Qiu, Z., Zheng, Z., Zhu, H., Chen, E.: Exploring large language model for graph data understanding in online job recommendations. arXiv preprint arXiv:2307.05722 (2023)

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2023

Closest in time.

Du, Y., Luo, D., Yan, R., Liu, H., Song, Y., Zhu, H., Zhang, J.: Enhancing job recommendation through llm-based generative adversarial networks. arXiv preprint arXiv:2307.10747 (2023)

Original

2023

Closest in time.

Zhang, J., Xie, R., Hou, Y., Zhao, W.X., Lin, L., Wen, J.: Recommendation as instruction following: A large language model empowered recommendation approach. CoRR abs/2305.07001

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2023

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Li, L., Zhang, Y., Chen, L.: Prompt distillation for efficient llm-based recommendation. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 1348–1357 (2023)

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Wang, L., Lim, E.: The whole is better than the sum: Using aggregated demonstrations in in-context learning for sequential recommendation. CoRR abs/2403.10135

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