Explaining Neural News Recommendation with Attributions onto Reading Histories
Article No.: 7, Pages 1 - 25
Abstract
An important aspect of responsible recommendation systems is the transparency of the prediction mechanisms. This is a general challenge for deep-learning-based systems such as the currently predominant neural news recommender architectures, which are optimized to predict clicks by matching candidate news items against users’ reading histories. Such systems achieve state-of-the-art click-prediction performance, but the rationale for their decisions is difficult to assess. At the same time, the economic and societal impact of these systems makes such insights very much desirable.
In this article, we ask the question to what extent the recommendations of current news recommender systems are actually based on content-related evidence from reading histories. We approach this question from an explainability perspective. Building on the concept of integrated gradients, we present a neural news recommender that can accurately attribute individual recommendations to news items and words in input reading histories while maintaining a top scoring click-prediction performance.
Using our method as a diagnostic tool, we find that: (a), a substantial number of users’ clicks on news are not explainable from reading histories, and many history-explainable items are actually skipped; (b), while many recommendations are based on content-related evidence in histories, for others the model does not attend to reasonable evidence, and recommendations stem from a spurious bias in user representations. Our code is publicly available at https://github.com/lucasmllr/xnrs.
References
[1]
Samira Abnar and Willem Zuidema. 2020. Quantifying Attention Flow in Transformers. In Proceedings of the 58th ACL. ACL, 4190–4197. DOI:
[2]
Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, and Xing Xie. 2019. Neural News Recommendation with Long- and Short-term User Representations. In Proceedings of the 57th ACL. ACL, 336–345. https://aclanthology.org/P19-1033
[3]
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein. 2020. A Diagnostic Study of Explainability Techniques for Text Classification. In Proceedings of EMNLP. ACL, 3256–3274. DOI:
[4]
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek. 2015. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation. PLoS ONE 10, 7 (2015), 1–46. DOI:
[5]
Jasmijn Bastings and Katja Filippova. 2020. The Elephant in the Interpretability Room: Why Use Attention as Explanation When We Have Saliency Methods? In Proceedings of the 3rd BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP. ACL, 149–155. DOI:
[6]
Alexander Binder, Grégoire Montavon, Sebastian Bach, Klaus-Robert Müller, and Wojciech Samek. 2016. Layer-wise relevance propagation for neural networks with local renormalization layers. arXiv:1604.00825. Retrieved from https://arxiv.org/abs/1604.00825
[7]
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics 5 (2017), 135–146. DOI:
[8]
Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018. Neural Attentional Rating Regression with Review-Level Explanations. In Proceedings of WWW. WWW Steering Committee, 1583–1592. DOI:
[9]
Hung-Hsuan Chen, Chu-An Chung, Hsin-Chien Huang, and Wen Tsui. 2017. Common Pitfalls in Training and Evaluating Recommender Systems. SIGKDD Explorations Newsletter 19, 1 (Sep. 2017), 37–45. DOI:
[10]
Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023. Bias and Debias in Recommender System: A Survey and Future Directions. ACM Transactions on Information Systems 41, 3 (Feb. 2023), Article 67, 39 pages. DOI:
[11]
Abhinandan S. Das, Mayur Datar, Ashutosh Garg, and Shyam Rajaram. 2007. Google News Personalization: Scalable Online Collaborative Filtering. In Proceedings of the 16th WWW. ACM, New York, NY, 271–280. DOI:
[12]
Fernando Diaz. 2021. On Evaluating Session-Based Recommendation with Implicit Feedback. In Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop (PERSPECTIVES ’21).
[13]
Jingtao Ding, Yuhan Quan, Xiangnan He, Yong Li, and Depeng Jin. 2019. Reinforced Negative Sampling for Recommendation with Exposure Data. In Proceedings of the 28th IJCAI. AAAI Press, 2230–2236. DOI:
[14]
Finale Doshi-Velez and Been Kim. 2017. Towards a rigorous science of interpretable machine learning. arXiv:1702.08608. Retrieved from https://arxiv.org/abs/1702.08608
[15]
Rotem Dror, Segev Shlomov, and Roi Reichart. 2019. Deep Dominance – How to Properly Compare Deep Neural Models. In Proceedings of the 57th ACL. ACL, 2773–2785. DOI: https://aclanthology.org/P19-1266
[16]
Seth Flaxman, Sharad Goel, and Justin M. Rao. 2016. Filter Bubbles, Echo Chambers, and Online News Consumption. Public Opinion Quarterly 80, S1 (2016), 298–320. DOI:. https://academic.oup.com/poq/article-pdf/80/S1/298/17120810/nfw006.pdf
[17]
Jingyue Gao, Xiting Wang, Yasha Wang, and Xing Xie. 2019. Explainable Recommendation through Attentive Multi-View Learning. Proceedings of the AAAI Conference on Artificial Intelligence 33, 1 (Jul. 2019), 3622–3629. DOI:
[18]
Shansan Gong and Kenny Q. Zhu. 2022. Positive, Negative and Neutral: Modeling Implicit Feedback in Session-Based News Recommendation. In Proceedings of the 45th SIGIR. ACM, New York, NY, 1185–1195. DOI:
[19]
Jon Atle Gulla, Lemei Zhang, Peng Liu, Özlem Özgöbek, and Xiaomeng Su. 2017. The Adressa dataset for news recommendation. In Proceedings of the International Conference on Web Intelligence (WI ’17). ACM, New York, NY, 1042–1048. DOI:
[20]
Lucien Heitz, Juliane A. Lischka, Alena Birrer, Bibek Paudel, Suzanne Tolmeijer, Laura Laugwitz, and Abraham Bernstein. 2022. Benefits of Diverse News Recommendations for Democracy: A User Study. Digital Journalism 10, 10 (2022), 1710–1730. DOI:
[21]
Natali Helberger. 2019. On the Democratic Role of News Recommenders. Digital Journalism 7, 8 (2019), 993–1012. DOI:
[22]
Katja Hofmann, Anne Schuth, Alejandro Bellogín, and Maarten de Rijke. 2014. Effects of Position Bias on Click-Based Recommender Evaluation. In Proceedings of the 36th ECIR. Springer International Publishing. DOI:
[23]
Linmei Hu, Siyong Xu, Chen Li, Cheng Yang, Chuan Shi, Nan Duan, Xing Xie, and Ming Zhou. 2020. Graph Neural News Recommendation with Unsupervised Preference Disentanglement. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault (Eds.), ACM, New York, NY, 4255–4264. DOI:
[24]
Andreea Iana, Goran Glavaš, and Heiko Paulheim. 2023. Simplifying content-based neural news recommendation: On User modeling and training objectives. arXiv:2304.03112. Retrieved from https://arxiv.org/abs/2304.03112
[25]
Sarthak Jain and Byron C. Wallace. 2019. Attention Is Not Explanation. In Proceedings of NAACL. ACL, 3543–3556. DOI:
[26]
M. Jenders, T. Lindhauer, G. Kasneci, R. Krestel, and F. Naumann. 2015. A Serendipity Model for News Recommendation. In KI 2015: Advances in Artificial Intelligence. Steffen Hölldobler, Rafael Peñaloza, and Sebastian Rudolph (Eds.), Springer International Publishing, 111–123.
[27]
Jongwon Jeong, Jeong Choi, Hyunsouk Cho, and Sehee Chung. 2022. FPAdaMetric: False-Positive-Aware Adaptive Metric Learning for Session-Based Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence 36, 4 (Jun. 2022), 4039–4047. DOI:
[28]
Hao Jiang, Chuanzhen Li, Juanjuan Cai, and Jingling Wang. 2023. RCENR: A Reinforced and Contrastive Heterogeneous Network Reasoning Model for Explainable News Recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (Taipei, Taiwan) (SIGIR ’23). ACM, New York, NY, 1710–1720. DOI:
[29]
Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, and Geri Gay. 2005. Accurately Interpreting Clickthrough Data as Implicit Feedback. In Proceedings of the 28th SIGIR. ACM, New York, NY, 154–161. DOI:
[30]
Thorsten Joachims, Adith Swaminathan, and Tobias Schnabel. 2017. Unbiased Learning-to-Rank with Biased Feedback. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM ’17). ACM, New York, NY, 781–789. DOI:
[31]
Mozhgan Karimi, Dietmar Jannach, and Michael Jugovac. 2018. News Recommender Systems – Survey and Roads Ahead. Information Processing & Management 54, 6 (2018), 1203–1227. DOI:
[32]
Evan Kirshenbaum, George Forman, and Michael Dugan. 2012. A Live Comparison of Methods for Personalized Article Recommendation at Forbes.com. In Machine Learning and Knowledge Discovery in Databases. Peter A. Flach, Tijl De Bie, and Nello Cristianini (Eds.), Springer, Berlin, 51–66. DOI:
[33]
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2016. Visualizing and Understanding Neural Models in NLP. In Proceedings of NAACL. ACL, 681–691. DOI:
[34]
Lihong Li, Wei Chu, John Langford, and Robert E. Schapire. 2010. A Contextual-Bandit Approach to Personalized News Article Recommendation. In Proceedings of the 19th WWW. ACM, New York, NY, 661–670. DOI:
[35]
Jianxun Lian, Fuzheng Zhang, Xing Xie, and Guangzhong Sun. 2018. Towards Better Representation Learning for Personalized News Recommendation: A Multi-Channel Deep Fusion Approach. In Proceedings of the 27th IJCAI. IJCAI Organization, 3805–3811. DOI:
[36]
Zachary C. Lipton. 2016. The mythos of model interpretability. arXiv:1606.03490. Retrieved from https://arxiv.org/abs/1606.03490
[37]
Danyang Liu, Jianxun Lian, Shiyin Wang, Ying Qiao, Jiun-Hung Chen, Guangzhong Sun, and Xing Xie. 2020. KRED: Knowledge-Aware Document Representation for News Recommendations. In Proceedings of the 14th RecSys. ACM, New York, NY, 200–209. DOI:
[38]
Jiahui Liu, Peter Dolan, and Elin Rønby Pedersen. 2010. Personalized News Recommendation Based on Click Behavior. In Proceedings of the 15th International Conference on Intelligent User Interfaces. ACM, New York, NY, 31–40. DOI:
[39]
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A robustly optimized BERT pretraining approach. arXiv:1907.11692. Retrieved from https://arxiv.org/abs/1907.11692
[40]
Hongyu Lu, Min Zhang, and Shaoping Ma. 2018b. Between Clicks and Satisfaction: Study on Multi-Phase User Preferences and Satisfaction for Online News Reading. In The 41st SIGIR. ACM, New York, NY, 435–444. DOI:
[41]
Yichao Lu, Ruihai Dong, and Barry Smyth. 2018. Coevolutionary Recommendation Model: Mutual Learning between Ratings and Reviews. In Proceedings of WWW. WWW Steering Committee, 773–782. DOI:
[42]
Silvia Milano, Mariarosaria Taddeo, and Luciano Floridi. 2020. Recommender Systems and Their Ethical Challenges. Ai & Society 35 (2020), 957–967.
[43]
Lucas Moeller, Dmitry Nikolaev, and Sebastian Padó. 2023. An Attribution Method for Siamese Encoders. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Houda Bouamor, Juan Pino, and Kalika Bali (Eds.), ACL, 15818–15827. DOI:
[44]
Lucas Moeller, Dmitry Nikolaev, and Sebastian Padó. 2024. Approximate Attributions for Off-the-Shelf Siamese Transformers. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). Yvette Graham and Matthew Purver (Eds.), ACL, 2059–2071. DOI: https://aclanthology.org/2024.eacl-long.125
[45]
W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu. 2019. Definitions, Methods, and Applications in Interpretable Machine Learning. Proceedings of the National Academy of Sciences 116, 44 (2019), 22071–22080. DOI:
[46]
Judith Möller, Damian Trilling, Natali Helberger, and Bram van Es. 2018. Do Not Blame it on the Algorithm: An Empirical Assessment of Multiple Recommender Systems and Their Impact on Content Diversity. Information, Communication & Society 21, 7 (2018), 959–977. DOI:
[47]
Keunchan Park, Jisoo Lee, and Jaeho Choi. 2017. Deep Neural Networks for News Recommendations. In Proceedings of CIKM. ACM, New York, NY, 2255–2258. DOI:
[48]
Georgina Peake and Jun Wang. 2018. Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems. In Proceedings of the 24th SIGKDD. ACM, New York, NY, 2060–2069. DOI:
[49]
Fernando Benjamin Perez Maurera, Maurizio Ferrari Dacrema, and Paolo Cremonesi. 2022. Towards the Evaluation of Recommender Systems with Impressions. In Proceedings of the 16th RecSys. ACM, New York, NY, 610–615. DOI:
[50]
Tao Qi, Fangzhao Wu, Chuhan Wu, and Yongfeng Huang. 2021. PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity. 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). Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.), ACL, 5457–5467. DOI:
[51]
Tao Qi, Fangzhao Wu, Chuhan Wu, and Yongfeng Huang. 2022a. News Recommendation with Candidate-aware User Modeling. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22). ACM, New York, NY, 1917–1921. DOI:
[52]
Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, and Xing Xie. 2020. Privacy-Preserving News Recommendation Model Learning. In Proceedings of the Findings of the Association for Computational Linguistics (EMNLP ’20), Trevor Cohn, Yulan He, and Yang Liu (Eds.), ACL, 1423–1432. DOI:
[53]
Tao Qi, Fangzhao Wu, Chuhan Wu, Peijie Sun, Le Wu, Xiting Wang, Yongfeng Huang, and Xing Xie. 2022. ProFairRec: Provider Fairness-Aware News Recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22). ACM, New York, NY, 1164–1173. DOI:
[54]
Shaina Raza, Syed Raza Bashir, and Usman Naseem. 2022. Accuracy Meets Diversity in a News Recommender System. In Proceedings of the 29th International Conference on Computational Linguistics. International Committee on Computational Linguistics, 3778–3787. DOI: https://aclanthology.org/2022.coling-1.332
[55]
Shaina Raza and Chen Ding. 2021. News Recommender System: A Review of Recent Progress, Challenges, and Opportunities. Artificial Intelligence Review 55 (2021), 749–800. DOI:
[56]
Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). ACL, 3982–3992. DOI:
[57]
Mark O. Riedl. 2019. Human-centered artificial intelligence and machine learning. arXiv:1901.11184. Retrieved from https://arxiv.org/abs/1901.11184
[58]
Alan Said, Jimmy Lin, Alejandro Bellogín, and Arjen de Vries. 2013. A Month in the Life of a Production News Recommender System. In Proceedings of the 2013 Workshop on Living Labs for Information Retrieval Evaluation (LivingLab ’13). ACM, New York, NY, 7–10. DOI:
[59]
Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020. Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback. In Proceedings of the 13th WSDM. ACM, New York, NY, 501–509. DOI:
[60]
Tobias Schnabel, Paul N. Bennett, and Thorsten Joachims. 2019. Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory. In Proceedings of the 12th WSDM. ACM, New York, NY, 546–554. DOI:
[61]
Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu. 2017. Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction. In Proceedings of the 11th RecSys. ACM, New York, NY, 297–305. DOI:
[62]
Karthik Shivaram, Ping Liu, Matthew Shapiro, Mustafa Bilgic, and Aron Culotta. 2022. Reducing Cross-Topic Political Homogenization in Content-Based News Recommendation. In Proceedings of the 16th ACM Conference on Recommender Systems (RecSys ’22). ACM, New York, NY, 220–228. DOI:
[63]
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic Attribution for Deep Networks. In Proceedings of the 34th ICML. JMLR, 3319–3328.
[64]
Chang-You Tai, Liang-Ying Huang, Chien-Kun Huang, and Lun-Wei Ku. 2021a. User-Centric Path Reasoning towards Explainable Recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’21). ACM, New York, NY, 879–889. DOI:
[65]
Chang-You Tai, Liang-Ying Huang, Chien-Kun Huang, and Lun-Wei Ku. 2021b. User-Centric Path Reasoning towards Explainable Recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’21). ACM, New York, NY, 879–889. DOI:
[66]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need. In Proceedings of the 31st NIPS. Curran Associates Inc., 6000–6010. DOI: https://dl.acm.org/doi/10.5555/3295222.3295349
[67]
Sanne Vrijenhoek, Gabriel Bénédict, Mateo Gutierrez Granada, Daan Odijk, and Maarten De Rijke. 2022. RADio – Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations. In Proceedings of the 16th ACM Conference on Recommender Systems (RecSys ’22). ACM, New York, NY, 208–219. DOI:
[68]
Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018. DKN: Deep Knowledge-Aware Network for News Recommendation. In Proceedings of WWW. WWW Steering Committee, 1835–1844. DOI:
[69]
Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua. 2021. Clicks Can Be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue. In Proceedings of the 44th SIGIR. ACM, New York, NY, 1288–1297. DOI:
[70]
Hongyi Wen, Longqi Yang, and Deborah Estrin. 2019. Leveraging Post-Click Feedback for Content Recommendations. In Proceedings of the 13th RecSys. ACM, New York, NY, 278–286. DOI:
[71]
Sarah Wiegreffe and Yuval Pinter. 2019. Attention is not not Explanation. In Proceedings of the 2019 EMNLP. ACL, 11–20. DOI:
[72]
Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, and Xing Xie. 2019b. Neural News Recommendation with Attentive Multi-View Learning. In Proceedings of the 28th IJCAI. AAAI Press, 3863–3869. DOI:
[73]
Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, and Xing Xie. 2019c. NPA: Neural News Recommendation with Personalized Attention. In Proceedings of the 25th SIGKDD. ACM, New York, NY, 2576–2584. DOI:
[74]
Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang, and Xing Xie. 2019d. Neural News Recommendation with Multi-Head Self-Attention. In Proceedings of EMNLP. ACL, 6389–6394. DOI:
[75]
Chuhan Wu, Fangzhao Wu, Xiangnan He, and Yongfeng Huang. 2022b. DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning. In Findings of the Association for Computational Linguistics (EMNLP ’22). Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (Eds.), ACL, 2933–2938. DOI:
[76]
Chuhan Wu, Fangzhao Wu, Yongfeng Huang, and Xing Xie. 2023. Personalized News Recommendation: Methods and Challenges. ACM Transactions on Information Systems 41, 1 (Jan. 2023), Article 24, 50 pages. DOI:
[77]
Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. 2020b. SentiRec: Sentiment Diversity-aware Neural News Recommendation. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing. Kam-Fai Wong, Kevin Knight, and Hua Wu (Eds.), ACL, 44–53. https://aclanthology.org/2020.aacl-main.6
[78]
Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. 2021a. Empowering News Recommendation with Pre-Trained Language Models. In Proceedings of the 44th SIGIR. ACM, New York, NY, 1652–1656. DOI:
[79]
Chuhan Wu, Fangzhao Wu, Tao Qi, Chao Zhang, Yongfeng Huang, and Tong Xu. 2022c. MM-Rec: Visiolinguistic Model Empowered Multimodal News Recommendation. In Proceedings of the 45th SIGIR. ACM, New York, NY, 2560–2564. DOI:
[80]
Chuhan Wu, Fangzhao Wu, Xiting Wang, Yongfeng Huang, and Xing Xie. 2021b. Fairness-aware News Recommendation with Decomposed Adversarial Learning. Proceedings of the AAAI Conference on Artificial Intelligence 35, 5 (May 2021), 4462–4469. DOI:
[81]
Chuhan Wu, Fangzhao Wu, Yang Yu, Tao Qi, Yongfeng Huang, and Qi Liu. 2021c. NewsBERT: Distilling Pre-Trained Language Model for Intelligent News Application. In Findings of EMNLP. ACL, 3285–3295. DOI:
[82]
Di Wu, Wasi Uddin Ahmad, and Kai-Wei Chang. 2022. Pre-trained language models for keyphrase generation: A thorough empirical study. arXiv:2212.10233. Retrieved from https://arxiv.org/abs/2212.10233
[83]
Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, and Ming Zhou. 2020a. MIND: A Large-scale Dataset for News Recommendation. In Proceedings of ACL. ACL, 3597–3606. https://aclanthology.org/2020.acl-main.331
[84]
Libing Wu, Cong Quan, Chenliang Li, Qian Wang, Bolong Zheng, and Xiangyang Luo. 2019a. A Context-Aware User-Item Representation Learning for Item Recommendation. ACM Transactions on Information Systems 37, 2 (Jan. 2019), Article 22, 29 pages. DOI:
[85]
Ruobing Xie, Cheng Ling, Yalong Wang, Rui Wang, Feng Xia, and Leyu Lin. 2021. Deep Feedback Network for Recommendation. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI ’20). 2519–2525.
[86]
Hongyan Xu, Qiyao Peng, Hongtao Liu, Yueheng Sun, and Wenjun Wang. 2023. Group-Based Personalized News Recommendation with Long- and Short-Term Fine-Grained Matching. ACM Transactions on Information Systems 42, 1 (Aug. 2023), Article 3, 27 pages. DOI:
[87]
Xing Yi, Liangjie Hong, Erheng Zhong, Nanthan Nan Liu, and Suju Rajan. 2014. Beyond Clicks: Dwell Time for Personalization. In Proceedings of the 8th RecSys. ACM, New York, NY, 113–120. DOI:
[88]
Yang Yu, Fangzhao Wu, Chuhan Wu, Jingwei Yi, and Qi Liu. 2022. Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation. In Proceedings of EMNLP. ACL, 5478–5489. https://aclanthology.org/2022.emnlp-main.368
[89]
Yongfeng Zhang and Xu Chen. 2020. Explainable Recommendation: A Survey and New Perspectives. Foundations and Trends in Information Retrieval 14, 1 (Mar. 2020), 1–101. DOI:
[90]
Qian Zhao, Shuo Chang, F. Maxwell Harper, and Joseph A. Konstan. 2016. Gaze Prediction for Recommender Systems. In Proceedings of the 10th RecSys. ACM, New York, NY, 131–138. DOI:
[91]
Qian Zhao, Martijn C. Willemsen, Gediminas Adomavicius, F. Maxwell Harper, and Joseph A. Konstan. 2018. Interpreting User Inaction in Recommender Systems. In Proceedings of the 12th RecSys. ACM, New York, NY, 40–48. DOI:
[92]
Ziwei Zhu, Yun He, Xing Zhao, and James Caverlee. 2021. Popularity Bias in Dynamic Recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD ’21). ACM, New York, NY, 2439–2449. DOI:
Index Terms
- Explaining Neural News Recommendation with Attributions onto Reading Histories
Recommendations
Personalized News Recommendation: Methods and Challenges
Personalized news recommendation is important for users to find interesting news information and alleviate information overload. Although it has been extensively studied over decades and has achieved notable success in improving user experience, there are ...
News Session-Based Recommendations using Deep Neural Networks
DLRS 2018: Proceedings of the 3rd Workshop on Deep Learning for Recommender SystemsNews recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, ...
Personalized news recommendation: a review and an experimental investigation
Special issue on Community Analysis and Information RecommendationOnline news articles, as a new format of press releases, have sprung up on the Internet. With its convenience and recency, more and more people prefer to read news online instead of reading the paper-format press releases. However, a gigantic amount of ...
Comments
Information & Contributors
Information
Published In
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 03 January 2025
Online AM: 18 June 2024
Accepted: 11 April 2024
Revised: 02 February 2024
Received: 30 June 2023
Published in TIST Volume 16, Issue 1
Check for updates
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 305Total Downloads
- Downloads (Last 12 months)305
- Downloads (Last 6 weeks)65
Reflects downloads up to 29 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in