Academic literature on the topic 'Explainable recommendation'
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Journal articles on the topic "Explainable recommendation"
Xie, Lijie, Zhaoming Hu, Xingjuan Cai, Wensheng Zhang, and Jinjun Chen. "Explainable recommendation based on knowledge graph and multi-objective optimization." Complex & Intelligent Systems 7, no. 3 (March 6, 2021): 1241–52. http://dx.doi.org/10.1007/s40747-021-00315-y.
Full textLeal, Fátima, Bruno Veloso, Benedita Malheiro, Juan C. Burguillo, Adriana E. Chis, and Horacio González-Vélez. "Stream-based explainable recommendations via blockchain profiling." Integrated Computer-Aided Engineering 29, no. 1 (December 28, 2021): 105–21. http://dx.doi.org/10.3233/ica-210668.
Full textKido, Shunsuke, Ryuji Sakamoto, and Masayoshi Aritsugi. "Making Use of More Reviews Skillfully in Explaninable Recommendation Gerneration." journal of Data Intelligence 2, no. 4 (November 2021): 434–47. http://dx.doi.org/10.26421/jdi2.4-3.
Full textSana, Saba, and Mohammad Shoaib. "Trustworthy Explainable Recommendation Framework for Relevancy." Computers, Materials & Continua 73, no. 3 (2022): 5887–909. http://dx.doi.org/10.32604/cmc.2022.028046.
Full textZheng, Xiaolin, Menghan Wang, Chaochao Chen, Yan Wang, and Zhehao Cheng. "EXPLORE: EXPLainable item-tag CO-REcommendation." Information Sciences 474 (February 2019): 170–86. http://dx.doi.org/10.1016/j.ins.2018.09.054.
Full textAi, Qingyao, Vahid Azizi, Xu Chen, and Yongfeng Zhang. "Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation." Algorithms 11, no. 9 (September 13, 2018): 137. http://dx.doi.org/10.3390/a11090137.
Full textWang, Tongxuan, Xiaolong Zheng, Saike He, Zhu Zhang, and Desheng Dash Wu. "Learning user-item paths for explainable recommendation." IFAC-PapersOnLine 53, no. 5 (2020): 436–40. http://dx.doi.org/10.1016/j.ifacol.2021.04.119.
Full textZhang, Yongfeng, and Xu Chen. "Explainable Recommendation: A Survey and New Perspectives." Foundations and Trends® in Information Retrieval 14, no. 1 (2020): 1–101. http://dx.doi.org/10.1561/1500000066.
Full textGao, Jingyue, Xiting Wang, Yasha Wang, and Xing Xie. "Explainable Recommendation through Attentive Multi-View Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3622–29. http://dx.doi.org/10.1609/aaai.v33i01.33013622.
Full textWang, Xiang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, and Tat-Seng Chua. "Explainable Reasoning over Knowledge Graphs for Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5329–36. http://dx.doi.org/10.1609/aaai.v33i01.33015329.
Full textDissertations / Theses on the topic "Explainable recommendation"
Hsu, Pei-Ying, and 許珮瑩. "A Novel Explainable Mutual Fund Recommendation System Based on Deep Learning Techniques with Knowledge Graph Embeddings." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/wur49w.
Full text國立交通大學
資訊管理研究所
107
Since deep learning based models have gained success in various fields during recent years, many recommendation systems also start to take advantage of the deep learning techniques. However, while the deep learning based recommendation systems have achieved high recommendation performance, the lack of interpretability may reduce users' trust and satisfaction, while limiting the model to wide adoption in the real world. As a result, to strike a balance between high accuracy and interpretability, or even obtain both of them at the same time, has become a popular issue among the researches of recommendation systems. In this thesis, we would like to predict and recommend the funds that would be purchased by the customers in the next month, while providing explanations simultaneously. To achieve the goal, we leverage the structure of knowledge graph, and take advantage of deep learning techniques to embed customers and funds features to a unified latent space. We fully utilize the structure knowledge which cannot be learned by the traditional deep learning models, and get the personalized recommendations and explanations. Moreover, we extend the explanations to more complex ones by changing the training procedure of the model, and proposed a measure to rate for the customized explanations while considering strength and uniqueness of the explanations at the same time. Finally, we regard that the knowledge graph based structure could be extended to other applications, and proposed some possible special recommendations accordingly. By evaluating on the dataset of mutual fund transaction records, we verify the effectiveness of our model to provide precise recommendations, and also evaluate the assumptions that our model could utilize the structure knowledge well. Last but not least, we conduct some case study of explanations to demonstrate the effectiveness of our model to provide usual explanations, complex explanations, and other special recommendations.
Books on the topic "Explainable recommendation"
Chen, Xu, and Yongfeng Zhang. Explainable Recommendation: A Survey and New Perspectives. Now Publishers, 2020.
Find full textBook chapters on the topic "Explainable recommendation"
Zong, Xiaoning, Yong Liu, Yonghui Xu, Yixin Zhang, Zhiqi Shen, Yonghua Yang, and Lizhen Cui. "SAER: Sentiment-Opinion Alignment Explainable Recommendation." In Database Systems for Advanced Applications, 315–22. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00126-0_24.
Full textZong, Xiaoning, Yong Liu, Yonghui Xu, Yixin Zhang, Zhiqi Shen, Yonghua Yang, and Lizhen Cui. "SAER: Sentiment-Opinion Alignment Explainable Recommendation." In Database Systems for Advanced Applications, 315–22. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00126-0_24.
Full textQureshi, M. Atif, and Derek Greene. "Lit@EVE: Explainable Recommendation Based on Wikipedia Concept Vectors." In Machine Learning and Knowledge Discovery in Databases, 409–13. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71273-4_41.
Full textWang, Huiying, Yue Kou, Derong Shen, and Tiezheng Nie. "An Explainable Recommendation Method Based on Multi-timeslice Graph Embedding." In Web Information Systems and Applications, 84–95. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60029-7_8.
Full textDu, Zhao, Lantao Hu, Xiaolong Fu, and Yongqi Liu. "Scalable and Explainable Friend Recommendation in Campus Social Network System." In Lecture Notes in Electrical Engineering, 457–66. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7618-0_45.
Full textMa, Ruixin, Guangyue Lv, Liang Zhao, Yunlong Ma, Hongyan Zhang, and Xiaobin Liu. "Multi-attention User Information Based Graph Convolutional Networks for Explainable Recommendation." In Knowledge Science, Engineering and Management, 201–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10983-6_16.
Full textYin, Ziyu, Yue Kou, Guangqi Wang, Derong Shen, and Tiezheng Nie. "Explainable Recommendation via Neural Rating Regression and Fine-Grained Sentiment Perception." In Web Information Systems and Applications, 580–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87571-8_50.
Full textChinone, Kosuke, and Atsuyoshi Nakamura. "An Explainable Recommendation Based on Acyclic Paths in an Edge-Colored Graph." In Lecture Notes in Computer Science, 40–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97546-3_4.
Full textZhang, Luhao, Ruiyu Fang, Tianchi Yang, Maodi Hu, Tao Li, Chuan Shi, and Dong Wang. "A Joint Framework for Explainable Recommendation with Knowledge Reasoning and Graph Representation." In Database Systems for Advanced Applications, 351–63. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00129-1_30.
Full textElazab, Fatma, Alia El Bolock, Cornelia Herbert, and Slim Abdennadher. "XReC: Towards a Generic Module-Based Framework for Explainable Recommendation Based on Character." In Highlights in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection, 17–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85710-3_2.
Full textConference papers on the topic "Explainable recommendation"
Tan, Juntao, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, and Yongfeng Zhang. "Counterfactual Explainable Recommendation." In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3459637.3482420.
Full textTangseng, Pongsate, and Takayuki Okatani. "Toward Explainable Fashion Recommendation." In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2020. http://dx.doi.org/10.1109/wacv45572.2020.9093367.
Full textGe, Yingqiang, Juntao Tan, Yan Zhu, Yinglong Xia, Jiebo Luo, Shuchang Liu, Zuohui Fu, Shijie Geng, Zelong Li, and Yongfeng Zhang. "Explainable Fairness in Recommendation." In SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3477495.3531973.
Full textChen, Zhongxia, Xiting Wang, Xing Xie, Mehul Parsana, Akshay Soni, Xiang Ao, and Enhong Chen. "Towards Explainable Conversational Recommendation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/414.
Full textLi, Lei, Yongfeng Zhang, and Li Chen. "Personalized Transformer for Explainable Recommendation." In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.acl-long.383.
Full textChen, Jiayi, Wen Wu, Wenxin Hu, Wei Zheng, and Liang He. "SSR: Explainable Session-based Recommendation." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9534196.
Full textTsukuda, Kosetsu, and Masataka Goto. "Explainable Recommendation for Repeat Consumption." In RecSys '20: Fourteenth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3383313.3412230.
Full textLi, Xueqi, Wenjun Jiang, Weiguang Chen, Jie Wu, Guojun Wang, and Kenli Li. "Directional and Explainable Serendipity Recommendation." In WWW '20: The Web Conference 2020. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3366423.3380100.
Full textHou, Hao, and Chongyang Shi. "Explainable Sequential Recommendation using Knowledge Graphs." In the 5th International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3338188.3338208.
Full textChen, Hongxu, Yicong Li, Xiangguo Sun, Guandong Xu, and Hongzhi Yin. "Temporal Meta-path Guided Explainable Recommendation." In WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3437963.3441762.
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