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In recent years, the introduction of knowledge graphs (KGs) has significantly advanced recommender systems by facilitating the discovery of potential associations between items.
Fast context-aware recommendations with factorization machines. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval . 635–644
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BPR: Bayesian personalized ranking from implicit feedback
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Personalized entity recommendation: A heterogeneous information network approach. In Proceedings of the 7th ACM international conference on Web search and data mining . 283–292
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Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the AAAI conference on artificial intelligence , Vol. 29
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Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining . 353–362
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Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval . 355–364
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Leveraging meta-path based context for top-n recommendation with a neural co-attention model. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining . 1531–1540
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Deepinf: Social influence prediction with deep learning. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining . 2110–2119
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DKN: Deep knowledge-aware network for news recommendation. In Proceedings of the 2018 world wide web conference . 1835–1844
Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018b · 2018
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Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In The world wide web conference . 151–161
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A survey on knowledge graph-based recommender systems
Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He. 2020 · 2020
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Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval . 639–648
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
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SimCSE: Simple Contrastive Learning of Sentence Embeddings. In Empirical Methods in Natural Language Processing (EMNLP)
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
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Joint knowledge pruning and recurrent graph convolution for news recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval . 51–60
Yu Tian, Yuhao Yang, Xudong Ren, Pengfei Wang, Fangzhao Wu, Qian Wang, and Chenliang Li. 2021 · 2021
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Alleviating cold-start problems in recommendation through pseudo-labelling over knowledge graph. In Proceedings of the 14th ACM international conference on web search and data mining . 931–939
Riku Togashi, Mayu Otani, and Shin’ichi Satoh. 2021 · 2021
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Learning intents behind interactions with knowledge graph for recommendation. In Proceedings of the web conference 2021 . 878–887
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Contrastive learning for sequential recommendation. In 2022 IEEE 38th international conference on data engineering (ICDE) . IEEE, 1259–1273
Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022 · 2022
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
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Recommender systems in the era of large language models (llms)
Zihuai Zhao, Wenqi Fan, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Zhen Wen, Fei Wang, Xiangyu Zhao, Jiliang Tang, et al · 2023
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Knowledge-refined Denoising Network for Robust Recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval . 362–371
Xinjun Zhu, Yuntao Du, Yuren Mao, Lu Chen, Yujia Hu, and Yunjun Gao. 2023 · 2023
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Large language models are zero-shot rankers for recommender systems. In European Conference on Information Retrieval . Springer, 364–381
Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2024 · 2024
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Knowledge graph contrastive learning for recommendation. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval . 1434–1443
Yuhao Yang, Chao Huang, Lianghao Xia, and Chenliang Li. 2022 · 2022
Cited alongside, same era.
Llm based generation of item-description for recommendation system. In Proceedings of the 17th ACM Conference on Recommender Systems . 1204–1207
Arkadeep Acharya, Brijraj Singh, and Naoyuki Onoe. 2023 · 2023
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Tallrec: An effective and efficient tuning framework to align large language model with recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems . 1007–1014
Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023 · 2023
Cited alongside, same era.
When large language models meet personalization: Perspectives of challenges and opportunities
Jin Chen, Zheng Liu, Xu Huang, Chenwang Wu, Qi Liu, Gangwei Jiang, Yuanhao Pu, Yuxuan Lei, Xiaolong Chen, Xingmei Wang, et al · 2023
Cited alongside, same era.
Recommender systems in the era of large language models (llms)
Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Jiliang Tang, and Qing Li. 2023 · 2023
Cited alongside, same era.
Large language models as zero-shot conversational recommenders. In Proceedings of the 32nd ACM international conference on information and knowledge management . 720–730
Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, and Julian McAuley. 2023 · 2023
Cited alongside, same era.
Large language models for generative recommendation: A survey and visionary discussions
Lei Li, Yongfeng Zhang, Dugang Liu, and Li Chen. 2023 · 2023
Cited alongside, same era.
Zero-shot next-item recommendation using large pretrained language models
Lei Wang and Ee-Peng Lim. 2023 · 2023
Cited alongside, same era.
Enhancing sequential recommendation via llm-based semantic embedding learning. In Companion Proceedings of the ACM on Web Conference 2024 . 103–111
Jun Hu, Wenwen Xia, Xiaolu Zhang, Chilin Fu, Weichang Wu, Zhaoxin Huan, Ang Li, Zuoli Tang, and Jun Zhou. 2024 · 2024
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Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 1395–1406
Sein Kim, Hongseok Kang, Seungyoon Choi, Donghyun Kim, Minchul Yang, and Chanyoung Park. 2024 · 2024
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Rella: Retrieval-enhanced large language models for lifelong sequential behavior comprehension in recommendation. In Proceedings of the ACM on Web Conference 2024 . 3497–3508
Jianghao Lin, Rong Shan, Chenxu Zhu, Kounianhua Du, Bo Chen, Shigang Quan, Ruiming Tang, Yong Yu, and Weinan Zhang. 2024b · 2024
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Representation learning with large language models for recommendation. In Proceedings of the ACM on Web Conference 2024 . 3464–3475
Xubin Ren, Wei Wei, Lianghao Xia, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2024 · 2024
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Unleashing the Power of Knowledge Graph for Recommendation via Invariant Learning. In Proceedings of the ACM on Web Conference 2024 . 3745–3755
Shuyao Wang, Yongduo Sui, Chao Wang, and Hui Xiong. 2024 · 2024
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Llmrec: Large language models with graph augmentation for recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining . 806–815
Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2024 · 2024
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A survey on large language models for recommendation
Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, et al · 2024
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Towards open-world recommendation with knowledge augmentation from large language models. In Proceedings of the 18th ACM Conference on Recommender Systems . 12–22
Yunjia Xi, Weiwen Liu, Jianghao Lin, Xiaoling Cai, Hong Zhu, Jieming Zhu, Bo Chen, Ruiming Tang, Weinan Zhang, and Yong Yu. 2024 · 2024
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Item-Difficulty-Aware Learning Path Recommendation: From a Real Walking Perspective. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 4167–4178
Haotian Zhang, Shuanghong Shen, Bihan Xu, Zhenya Huang, Jinze Wu, Jing Sha, and Shijin Wang. 2024 · 2024
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Qian Zhao, Hao Qian, Ziqi Liu, Gong-Duo Zhang, and Lihong Gu. 2024 · 2024
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Collaborative large language model for recommender systems. In Proceedings of the ACM on Web Conference 2024 . 3162–3172
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, and Jundong Li. 2024 · 2024
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