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The explainability of recommendation systems is crucial for enhancing user trust and satisfaction.
Language models are few-shot learners
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Attention is all you need
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Attention-driven factor model for explainable personalized recommendation
Chen, J., Zhuang, F., Hong, X., Ao, X., Xie, X., and He, Q · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Self-attentive sequential recommendation
Kang, W.-C., and McAuley, J · 2018
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Posthoc interpretability of learning to rank models using secondary training data
Singh, J., and Anand, A · 2018
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A reinforcement learning framework for explainable recommendation
Wang, X., Chen, Y., Yang, J., Wu, L., Wu, Z., and Xie, X · 2018
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Tem: Tree-enhanced embedding model for explainable recommendation
Wang, X., He, X., Feng, F., Nie, L., and Chua, T.-S · 2018
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Transparent, scrutable and explainable user models for personalized recommendation
Balog, K., Radlinski, F., and Arakelyan, S · 2019
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Personalized fashion recommendation with visual explanations based on multimodal attention network: Towards visually explainable recommendation
Chen, X., Chen, H., Xu, H., Zhang, Y., Cao, Y., Qin, Z., and Zha, H · 2019
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Explainable recommendation with personalized review retrieval and aspect learning
Cheng, H., Wang, S., Lu, W., Zhang, W., Zhou, M., Lu, K., and Liao, H · 2023
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Recommender systems in the era of large language models (llms)
Fan, W., Zhao, Z., Li, J., Liu, Y., Mei, X., Wang, Y., Tang, J., and Li, Q · 2023
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Large language models as zero-shot conversational recommenders
He, Z., Xie, Z., Jha, R., Steck, H., Liang, D., Feng, Y., Majumder, B. P., Kallus, N., and McAuley, J · 2023
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Recommender ai agent: Integrating large language models for interactive recommendations
Huang, X., Lian, J., Lei, Y., Yao, J., Lian, D., and Xie, X · 2023
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Personalized prompt learning for explainable recommendation
Li, L., Zhang, Y., and Chen, L · 2023
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Incorporating interpretability into latent factor models via fast influence analysis
Cheng, W., Shen, Y., Huang, L., and Zhu, Y · 2019
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Jointly learning explainable rules for recommendation with knowledge graph
Ma, W., Zhang, M., Cao, Y., Jin, W., Wang, C., Liu, Y., Ma, S., and Ren, X · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Reimers, N., and Gurevych, I · 2019
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Energy and policy considerations for deep learning in nlp
Strubell, E., Ganesh, A., and McCallum, A · 2019
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Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., and Jiang, P · 2019
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The fact: Taming latent factor models for explainability with factorization trees
Tao, Y., Jia, Y., Wang, N., and Wang, H · 2019
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Reinforcement knowledge graph reasoning for explainable recommendation
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Is chatgpt a good recommender? a preliminary study
Liu, J., Liu, C., Zhou, P., Lv, R., Zhou, K., and Zhang, Y · 2023
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Summary of chatgpt-related research and perspective towards the future of large language models
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Llama: Open and efficient foundation language models
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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
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Learning the explainable semantic relations via unified graph topic-disentangled neural networks
Wu, L., Zhao, H., Li, Z., Huang, Z., Liu, Q., and Chen, E · 2023
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A survey on large language models for recommendation
Wu, L., Zheng, Z., Qiu, Z., Wang, H., Gu, H., Shen, T., Qin, C., Zhu, C., Zhu, H., Liu, Q., et al · 2023
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Enhancing collaborative semantics of language model-driven recommendations via graph-aware learning
Guan, Z., Wu, L., Zhao, H., He, M., and Fan, J · 2024
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Langtopo: Aligning language descriptions of graphs with tokenized topological modeling
Guan, Z., Zhao, H., Wu, L., He, M., and Fan, J · 2024
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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
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Data-efficient fine-tuning for llm-based recommendation
Lin, X., Wang, W., Li, Y., Yang, S., Feng, F., Wei, Y., and Chua, T.-S · 2024
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Exploring large language model for graph data understanding in online job recommendations
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Explanation mining: Post hoc interpretability of latent factor models for recommendation systems
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