Готові списки джерел за темами / Explainable recommendation

Добірка наукової літератури з теми "Explainable recommendation"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Explainable recommendation"

1

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.

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Анотація:
AbstractRecommendation system is a technology that can mine user's preference for items. Explainable recommendation is to produce recommendations for target users and give reasons at the same time to reveal reasons for recommendations. The explainability of recommendations that can improve the transparency of recommendations and the probability of users choosing the recommended items. The merits about explainability of recommendations are obvious, but it is not enough to focus solely on explainability of recommendations in field of explainable recommendations. Therefore, it is essential to construct an explainable recommendation framework to improve the explainability of recommended items while maintaining accuracy and diversity. An explainable recommendation framework based on knowledge graph and multi-objective optimization is proposed that can optimize the precision, diversity and explainability about recommendations at the same time. Knowledge graph connects users and items through different relationships to obtain an explainable candidate list for target user, and the path between target user and recommended item is used as an explanation basis. The explainable candidate list is optimized through multi-objective optimization algorithm to obtain the final recommendation list. It is concluded from the results about experiments that presented explainable recommendation framework provides high-quality recommendations that contains high accuracy, diversity and explainability.
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2

Leal, 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.

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Анотація:
Explainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters – Memory-based and Model-based – using, as use cases, data streamed from two large tourism crowdsourcing platforms, namely Expedia and TripAdvisor. Building historical trust-based models of raters, our method is implemented as an external module and integrated with the collaborative filter through a post-recommendation component. The inter-user trust profiling history, traceability and authenticity are ensured by blockchain, since these profiles are stored as a smart contract in a private Ethereum network. Our empirical evaluation with HotelExpedia and Tripadvisor has consistently shown the positive impact of blockchain-based profiling on the quality (measured as recall) and transparency (determined via explanations) of recommendations.
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3

Kido, 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.

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Анотація:
There are a lot of reviews in the Internet, and existing explainable recommendation techniques use them. However, how to use reviews has not been so far adequately addressed. This paper proposes a new exploiting method of reviews in explainable recommendation generation. Our new method makes use of not only reviews written but also those referred to by users. This paper adopts two state-of-the-art explainable recommendation approaches and shows how to apply our method to them. Moreover, our method in this paper considers the possibility of making use of reviews which do not provide detailed review utilization. Our proposal can be applied to different explainable recommendation approaches, which is shown by adopting the two approaches, with reviews that do not necessarily provide their detailed utilization data. The evaluation with using Amazon reviews shows an improvement of the two explainable recommendation approaches. Our proposal is the first attempt to make use of reviews which are written or referred to by users in generating explainable recommendation. Particularly, this study does not suppose that reviews provide their detailed utilization data.
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Sana, 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.

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Zheng, 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.

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Ai, 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.

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Анотація:
Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms—especially the collaborative filtering (CF)- based approaches with shallow or deep models—usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely ignored recently due to the availability of vast amounts of data and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users’ historical behaviors and the knowledge is helpful for providing informed explanations regarding the recommended items. A great challenge for using knowledge bases for recommendation is how to integrate large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements in knowledge-base embedding (KBE) sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge for explanation. In this work, we propose to explain knowledge-base embeddings for explainable recommendation. Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items. Experimental results on real-world e-commerce datasets verified the superior recommendation performance and the explainability power of our approach compared with state-of-the-art baselines.
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Wang, 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.

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Zhang, 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.

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Gao, 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.

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Анотація:
Recommender systems have been playing an increasingly important role in our daily life due to the explosive growth of information. Accuracy and explainability are two core aspects when we evaluate a recommendation model and have become one of the fundamental trade-offs in machine learning. In this paper, we propose to alleviate the trade-off between accuracy and explainability by developing an explainable deep model that combines the advantages of deep learning-based models and existing explainable methods. The basic idea is to build an initial network based on an explainable deep hierarchy (e.g., Microsoft Concept Graph) and improve the model accuracy by optimizing key variables in the hierarchy (e.g., node importance and relevance). To ensure accurate rating prediction, we propose an attentive multi-view learning framework. The framework enables us to handle sparse and noisy data by co-regularizing among different feature levels and combining predictions attentively. To mine readable explanations from the hierarchy, we formulate personalized explanation generation as a constrained tree node selection problem and propose a dynamic programming algorithm to solve it. Experimental results show that our model outperforms state-of-the-art methods in terms of both accuracy and explainability.
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Wang, 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.

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Анотація:
Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user’s interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path.In this paper, we contribute a new model named Knowledgeaware Path Recurrent Network (KPRN) to exploit knowledge graph for recommendation. KPRN can generate path representations by composing the semantics of both entities and relations. By leveraging the sequential dependencies within a path, we allow effective reasoning on paths to infer the underlying rationale of a user-item interaction. Furthermore, we design a new weighted pooling operation to discriminate the strengths of different paths in connecting a user with an item, endowing our model with a certain level of explainability. We conduct extensive experiments on two datasets about movie and music, demonstrating significant improvements over state-of-the-art solutions Collaborative Knowledge Base Embedding and Neural Factorization Machine.
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Дисертації з теми "Explainable recommendation"

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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.

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Анотація:
碩士
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.
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Книги з теми "Explainable recommendation"

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Chen, Xu, and Yongfeng Zhang. Explainable Recommendation: A Survey and New Perspectives. Now Publishers, 2020.

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Частини книг з теми "Explainable recommendation"

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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.

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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.

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Qureshi, 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.

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Wang, 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.

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Du, 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.

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Ma, 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.

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Yin, 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.

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Chinone, 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.

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Zhang, 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.

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Elazab, 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.

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Тези доповідей конференцій з теми "Explainable recommendation"

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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.

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Tangseng, 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.

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3

Ge, 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.

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4

Chen, 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.

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Анотація:
Recent studies have shown that both accuracy and explainability are important for recommendation. In this paper, we introduce explainable conversational recommendation, which enables incremental improvement of both recommendation accuracy and explanation quality through multi-turn user-model conversation. We show how the problem can be formulated, and design an incremental multi-task learning framework that enables tight collaboration between recommendation prediction, explanation generation, and user feedback integration. We also propose a multi-view feedback integration method to enable effective incremental model update. Empirical results demonstrate that our model not only consistently improves the recommendation accuracy but also generates explanations that fit user interests reflected in the feedbacks.
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Li, 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.

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Chen, 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.

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Tsukuda, 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.

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Li, 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.

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Hou, 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.

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Chen, 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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