Abstract
ChatGPT has achieved remarkable success in natural language understanding. Considering that recommendation is indeed a conversation between users and the system with items as words, which has similar underlying pattern with ChatGPT, we design a new chat framework in item index level for the recommendation task. Our novelty mainly contains three parts: model, training and inference. For the model part, we adopt Generative Pre-training Transformer (GPT) as the sequential recommendation model and design a user modular to capture personalized information. For the training part, we adopt the two-stage paradigm of ChatGPT, including pre-training and fine-tuning. In the pre-training stage, we train GPT model by auto-regression. In the fine-tuning stage, we train the model with prompts, which include both the newly-generated results from the model and the user’s feedback. For the inference part, we predict several user interests as user representations in an autoregressive manner. For each interest vector, we recall several items with the highest similarity and merge the items recalled by all interest vectors into the final result. We conduct experiments with both offline public datasets and online A/B test to demonstrate the effectiveness of our proposed method.
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Notes
- 1.
https://www.kuaishou.com/en
- 2.
http://jmcauley.ucsd.edu/data/amazon/
- 3.
https://www.yelp.com/dataset
References
Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al.: A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. arXiv:2302.04023 (2023)
Cen, Y., Zhang, J., Zou, X., Zhou, C., Yang, H., Tang, J.: Controllable multi-interest framework for recommendation. In: Proceedings of the 26th ACM SIGKDD International Conference. pp. 2942–2951 (2020)
Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018)
Gao, Y., Sheng, T., Xiang, Y., Xiong, Y., Wang, H., Zhang, J.: Chat-rec: Towards interactive and explainable llms-augmented recommender system. arXiv preprint arXiv:2303.14524 (2023)
He, R., McAuley, J.: Fusing similarity models with markov chains for sparse sequential recommendation. In: 2016 IEEE ICDM. pp. 191–200. IEEE (2016)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)
Hyun, D., Park, C., Cho, J., Yu, H.: Beyond learning from next item: Sequential recommendation via personalized interest sustainability. In: Proceedings of the 31st ACM International CIKM. pp. 812–821 (2022)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems (TOIS) 20(4), 422–446 (2002)
Kang, W.C., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE international conference on data mining (ICDM). pp. 197–206. IEEE (2018)
Khalil, M., Er, E.: Will chatgpt get you caught? rethinking of plagiarism detection. arXiv preprint arXiv:2302.04335 (2023)
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. pp. 1419–1428 (2017)
Li, J., Zhang, W., Wang, T., Xiong, G., Lu, A., Medioni, G.: Gpt4rec: A generative framework for personalized recommendation and user interests interpretation. arXiv preprint arXiv:2304.03879 (2023)
Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: Stamp: short-term attention/memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD. pp. 1831–1839 (2018)
McAuley, J., Targett, C., Shi, Q., Van Den Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th international ACM SIGIR. pp. 43–52 (2015)
Qiu, R., Huang, Z., Yin, H., Wang, Z.: Contrastive learning for representation degeneration problem in sequential recommendation. In: Proceedings of the Fifteenth ACM ICDM. pp. 813–823 (2022)
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training (2018)
Rendle, S.: Factorization machines. In: 2010 IEEE International conference on data mining. pp. 995–1000. IEEE (2010)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th international conference on World wide web. pp. 811–820 (2010)
Shen, Y., Heacock, L., Elias, J., Hentel, K.D., Reig, B., Shih, G., Moy, L.: Chatgpt and other large language models are double-edged swords (2023)
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., Jiang, P.: Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM international CIKM. pp. 1441–1450 (2019)
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017)
Wang, L., Lim, E.P.: Zero-shot next-item recommendation using large pretrained language models. arXiv preprint arXiv:2304.03153 (2023)
Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., et al.: A survey on large language model based autonomous agents. Frontiers of Computer Science 18(6), 1–26 (2024)
Wu, C.Y., Ahmed, A., Beutel, A., Smola, A.J., Jing, H.: Recurrent recommender networks. In: Proceedings of the tenth ACM WSDM. pp. 495–503 (2017)
Wu, L., Li, S., Hsieh, C.J., Sharpnack, J.: Sse-pt: Sequential recommendation via personalized transformer. In: Fourteenth ACM Conference on Recommender Systems. pp. 328–337 (2020)
Wu, Y., Xie, R., Zhu, Y., Zhuang, F., Zhang, X., Lin, L., He, Q.: Personalized prompts for sequential recommendation. arXiv preprint arXiv:2205.09666 (2022)
Xie, X., Sun, F., Liu, Z., Gao, J., Ding, B., Cui, B.: Contrastive pre-training for sequential recommendation. arXiv preprint arXiv:2010.14395 (2020)
Xie, X., Sun, F., Liu, Z., Wu, S., Gao, J., Zhang, J., Ding, B., Cui, B.: Contrastive learning for sequential recommendation. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE). pp. 1259–1273. IEEE (2022)
Zhao, W.X., Hou, Y., Pan, X., Yang, C., Zhang, Z., Lin, Z., Zhang, J., Bian, S., Tang, J., Sun, W., et al.: Recbole 2.0: Towards a more up-to-date recommendation library. arXiv preprint arXiv:2206.07351 (2022)
Zhou, K., Wang, H., Zhao, W.X., Zhu, Y., Wang, S., Zhang, F., Wang, Z., Wen, J.R.: S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In: the 29th CIKM. pp. 1893–1902 (2020)
Zhou, K., Yu, H., Zhao, W.X., Wen, J.R.: Filter-enhanced mlp is all you need for sequential recommendation. In: Proceedings of the WWW. pp. 2388–2399 (2022)
Acknowledgment
This work is supported in part by National Key R&D Program of China (2023YFF0905402),National Natural Science Foundation of China (No. 62102420), Beijing Outstanding Young Scientist Program NO. BJJWZYJH012019100020098, Intelligent Social Governance Platform, Major Innovation & Planning Interdisciplinary Platform for the ”Double-First Class” Initiative, Renmin University of China, Public Computing Cloud, Renmin University of China, fund for building world-class universities (disciplines) of Renmin University of China, Intelligent Social Governance Platform.
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Zhang, Y. et al. (2024). RecGPT. In: Onizuka, M., et al. Database Systems for Advanced Applications. DASFAA 2024. Lecture Notes in Computer Science, vol 14854. Springer, Singapore. https://doi.org/10.1007/978-981-97-5569-1_15
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DOI: https://doi.org/10.1007/978-981-97-5569-1_15
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