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A Counterfactual Framework for Learning and Evaluating Explanations for Recommender Systems

Authors:

Veronika Bogina,

Liya Gurevitch,

Noam KoenigsteinAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 3723 - 3733

https://doi.org/10.1145/3589334.3645560

Published: 13 May 2024 Publication History

Abstract

In the field of recommender systems, explainability remains a pivotal yet challenging aspect. To address this, we introduce the Learning to eXplain Recommendations (LXR) framework, a post-hoc, model-agnostic approach designed for providing counterfactual explanations. LXR is compatible with any differentiable recommender algorithm and scores the relevance of user data in relation to recommended items. A distinctive feature of LXR is its use of novel self-supervised counterfactual loss terms, which effectively highlight the most influential user data responsible for a specific recommended item. Additionally, we propose several innovative counterfactual evaluation metrics specifically tailored for assessing the quality of explanations in recommender systems. Our code is available on our GitHub repository: https://github.com/DeltaLabTLV/LXR.

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References

[1]

Behnoush Abdollahi and Olfa Nasraoui. 2016. Explainable matrix factorization for collaborative filtering. In Proceedings of the 25th International Conference Companion on World Wide Web. 5--6.

Digital Library

[2]

Behnoush Abdollahi and Olfa Nasraoui. 2017. Using explainability for constrained matrix factorization. In Proceedings of the eleventh ACM conference on recommender systems. 79--83.

Digital Library

[3]

Samira Abnar and Willem Zuidema. 2020. Quantifying Attention Flow in Transformers. arXiv preprint arXiv:2005.00928 (2020).

[4]

Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. 2018. Sanity checks for saliency maps. Advances in neural information processing systems, Vol. 31 (2018).

[5]

Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, and Himabindu Lakkaraju. 2022. Openxai: Towards a transparent evaluation of model explanations. Advances in Neural Information Processing Systems, Vol. 35 (2022), 15784--15799.

[6]

Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. 2017. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, Vol. 39, 12 (2017), 2481--2495.

[7]

Sujoy Bag, Sri Krishna Kumar, and Manoj Kumar Tiwari. 2019. An efficient recommendation generation using relevant Jaccard similarity. Information Sciences, Vol. 483 (2019), 53--64.

Digital Library

[8]

Dor Bank, Noam Koenigstein, and Raja Giryes. 2023. Autoencoders. Machine learning for data science handbook: data mining and knowledge discovery handbook (2023), 353--374.

[9]

Oren Barkan. 2017. Bayesian neural word embedding. In Thirty-First AAAI Conference on Artificial Intelligence.

Digital Library

[10]

Oren Barkan, Omri Armstrong, Amir Hertz, Avi Caciularu, Ori Katz, Itzik Malkiel, and Noam Koenigstein. 2021a. GAM: Explainable Visual Similarity and Classification via Gradient Activation Maps. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 68--77.

Digital Library

[11]

Oren Barkan, Yuval Asher, Amit Eshel, Yehonatan Elisha, and Noam Koenigstein. 2023 b. Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Models. In IEEE International Conference on Data Mining (ICDM).

[12]

Oren Barkan, Yuval Asher, Amit Eshel, and Noam Koenigstein. 2023 a. Visual Explanations via Iterated Integrated Attributions. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 2073--2084.

[13]

Oren Barkan, Avi Caciularu, Ori Katz, and Noam Koenigstein. 2020a. Attentive item2vec: Neural attentive user representations. In ICASSP 2020--2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 3377--3381.

[14]

Oren Barkan, Avi Caciularu, Idan Rejwan, Ori Katz, Jonathan Weill, Itzik Malkiel, and Noam Koenigstein. 2020b. Cold Item Recommendations via Hierarchical Item2vec. In 2020 IEEE International Conference on Data Mining (ICDM). IEEE Computer Society, 912--917.

[15]

Oren Barkan, Avi Caciularu, Idan Rejwan, Ori Katz, Jonathan Weill, Itzik Malkiel, and Noam Koenigstein. 2021b. Representation learning via variational bayesian networks. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 78--88.

Digital Library

[16]

Oren Barkan, Yehonatan Elisha, Yuval Asher, Amit Eshel, and Noam Koenigstein. 2023 c. Visual Explanations via Iterated Integrated Attributions. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 2073--2084.

[17]

Oren Barkan, Yehonatan Elisha, Jonathan Weill, Yuval Asher, Amit Eshel, and Noam Koenigstein. 2023 d. Deep Integrated Explanations. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 57--67.

Digital Library

[18]

Oren Barkan, Yonatan Fuchs, Avi Caciularu, and Noam Koenigstein. 2020c. Explainable recommendations via attentive multi-persona collaborative filtering. In Proceedings of the 14th ACM Conference on Recommender Systems. 468--473.

Digital Library

[19]

Oren Barkan, Edan Hauon, Avi Caciularu, Ori Katz, Itzik Malkiel, Omri Armstrong, and Noam Koenigstein. 2021c. Grad-sam: Explaining transformers via gradient self-attention maps. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2882--2887.

Digital Library

[20]

Oren Barkan, Roy Hirsch, Ori Katz, Avi Caciularu, and Noam Koenigstein. 2021d. Anchor-based collaborative filtering. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2877--2881.

Digital Library

[21]

Oren Barkan, Roy Hirsch, Ori Katz, Avi Caciularu, Jonathan Weill, and Noam Koenigstein. 2021 e. Cold item integration in deep hybrid recommenders via tunable stochastic gates. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 994--999.

[22]

Oren Barkan, Roy Hirsch, Ori Katz, Avi Caciularu, Yoni Weill, and Noam Koenigstein. 2021 f. Cold start revisited: A deep hybrid recommender with cold-warm item harmonization. In ICASSP 2021--2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 3260--3264.

[23]

Oren Barkan, Ori Katz, and Noam Koenigstein. 2020d. Neural attentive multiview machines. In ICASSP 2020--2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 3357--3361.

[24]

Oren Barkan and Noam Koenigstein. 2016. Item2vec: neural item embedding for collaborative filtering. In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

[25]

Oren Barkan, Noam Koenigstein, Eylon Yogev, and Ori Katz. 2019a. CB2CF: a neural multiview content-to-collaborative filtering model for completely cold item recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems. 228--236.

Digital Library

[26]

Oren Barkan, Noam Razin, Itzik Malkiel, Ori Katz, Avi Caciularu, and Noam Koenigstein. 2020 e. Scalable attentive sentence pair modeling via distilled sentence embedding. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 3235--3242.

[27]

Oren Barkan, Tal Reiss, Jonathan Weill, Ori Katz, Roy Hirsch, Itzik Malkiel, and Noam Koenigstein. 2023 e. Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 20007--20018.

[28]

Oren Barkan, Idan Rejwan, Avi Caciularu, and Noam Koenigstein. 2020 f. Bayesian hierarchical words representation learning. arXiv preprint arXiv:2004.07126 (2020).

[29]

Oren Barkan, Tom Shaked, Yonatan Fuchs, and Noam Koenigstein. 2023 f. Modeling users' heterogeneous taste with diversified attentive user profiles. User Modeling and User-Adapted Interaction (2023), 1--31.

[30]

Oren Barkan, Shlomi Shvartzman, Noy Uzrad, Almog Elharar, Moshe Laufer, and Noam Koenigstein. 2023 g. InverSynth II: Sound matching via self-supervised synthesizer-proxy and inference-time finetuning. ISMIR.

[31]

Oren Barkan and David Tsiris. 2019. Deep synthesizer parameter estimation. In ICASSP 2019--2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 3887--3891.

[32]

Oren Barkan, David Tsiris, Ori Katz, and Noam Koenigstein. 2019b. Inversynth: Deep estimation of synthesizer parameter configurations from audio signals. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 27, 12 (2019), 2385--2396.

Digital Library

[33]

Mustafa Bilgic and Raymond J Mooney. 2005. Explaining recommendations: Satisfaction vs. promotion. In Beyond personalization workshop, IUI, Vol. 5. 153.

[34]

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, Vol. 33 (2020), 1877--1901.

[35]

Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 2020. End-to-end object detection with transformers. In European conference on computer vision. 213--229.

Digital Library

[36]

Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N Balasubramanian. 2018. Grad-cam: Generalized gradient-based visual explanations for deep convolutional networks. In 2018 IEEE winter conference on applications of computer vision (WACV). IEEE, 839--847.

[37]

Hila Chefer, Shir Gur, and Lior Wolf. 2021. Transformer interpretability beyond attention visualization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 782--791.

[38]

Tong Chen, Hongzhi Yin, Guanhua Ye, Zi Huang, Yang Wang, and Meng Wang. 2020. Try this instead: Personalized and interpretable substitute recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 891--900.

Digital Library

[39]

Xu Chen, Zheng Qin, Yongfeng Zhang, and Tao Xu. 2016. Learning to rank features for recommendation over multiple categories. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. 305--314.

Digital Library

[40]

Weiyu Cheng, Yanyan Shen, Linpeng Huang, and Yanmin Zhu. 2019. Incorporating interpretability into latent factor models via fast influence analysis. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 885--893.

Digital Library

[41]

Jifeng Dai, Yi Li, Kaiming He, and Jian Sun. 2016. R-fcn: Object detection via region-based fully convolutional networks. Advances in neural information processing systems, Vol. 29 (2016).

[42]

Alexandre Défossez, Neil Zeghidour, Nicolas Usunier, Léon Bottou, and Francis Bach. 2018. Sing: Symbol-to-instrument neural generator. Advances in neural information processing systems, Vol. 31 (2018).

[43]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).

[44]

Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).

[45]

Gideon Dror, Noam Koenigstein, Yehuda Koren, and Markus Weimer. 2012. The yahoo! music dataset and kdd-cup'11. In Proceedings of KDD Cup 2011. PMLR, 3--18.

[46]

Jesse Engel, Kumar Krishna Agrawal, Shuo Chen, Ishaan Gulrajani, Chris Donahue, and Adam Roberts. 2019. Gansynth: Adversarial neural audio synthesis. arXiv preprint arXiv:1902.08710 (2019).

[47]

Joseph Enguehard. 2023. Sequential Integrated Gradients: a simple but effective method for explaining language models. arXiv preprint arXiv:2305.15853 (2023).

[48]

Javier Ferrando, Gerard I Gállego, and Marta R Costa-jussà. 2022. Measuring the mixing of contextual information in the transformer. arXiv preprint arXiv:2203.04212 (2022).

[49]

Keren Gaiger, Oren Barkan, Shir Tsipory-Samuel, and Noam Koenigstein. 2023. Not All Memories Created Equal: Dynamic User Representations for Collaborative Filtering. IEEE Access, Vol. 11 (2023), 34746--34763.

[50]

Dvir Ginzburg, Itzik Malkiel, Oren Barkan, Avi Caciularu, and Noam Koenigstein. 2021. Self-supervised document similarity ranking via contextualized language models and hierarchical inference. arXiv preprint arXiv:2106.01186 (2021).

[51]

Bryce Goodman and Seth Flaxman. 2017. European Union regulations on algorithmic decision-making and a “right to explanation”. AI magazine, Vol. 38, 3 (2017), 50--57.

Digital Library

[52]

F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), Vol. 5, 4 (2015), 1--19.

[53]

Sergiu Hart. 1989. Shapley value. Springer.

[54]

Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. 2022. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 16000--16009.

[55]

Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), 770--778.

[56]

Xiangnan He, Tao Chen, Min-Yen Kan, and Xiao Chen. 2015. Trirank: Review-aware explainable recommendation by modeling aspects. In Proceedings of the 24th ACM international on conference on information and knowledge management. 1661--1670.

Digital Library

[57]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. 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.

Digital Library

[58]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 173--182.

Digital Library

[59]

Jonathan L Herlocker, Joseph A Konstan, and John Riedl. 2000. Explaining collaborative filtering recommendations. In Proceedings of the 2000 ACM conference on Computer supported cooperative work. 241--250.

Digital Library

[60]

Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE International Conference on Data Mining. Ieee, 263--272.

Digital Library

[61]

Yidan Hu, Yong Liu, Chunyan Miao, Gongqi Lin, and Yuan Miao. 2022. Aspect-guided syntax graph learning for explainable recommendation. IEEE Transactions on Knowledge and Data Engineering (2022).

Digital Library

[62]

Gao Huang, Zhuang Liu, and Kilian Q. Weinberger. 2017. Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017), 2261--2269.

[63]

Vassilis Kaffes, Dimitris Sacharidis, and Giorgos Giannopoulos. 2021. Model-agnostic counterfactual explanations of recommendations. In Proceedings of the 29th ACM conference on user modeling, adaptation and personalization. 280--285.

Digital Library

[64]

Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

[65]

Pang Wei Koh and Percy Liang. 2017. Understanding black-box predictions via influence functions. In International conference on machine learning. PMLR, 1885--1894.

[66]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer, Vol. 42, 8 (2009), 30--37.

Digital Library

[67]

Kundan Kumar, Rithesh Kumar, Thibault De Boissiere, Lucas Gestin, Wei Zhen Teoh, Jose Sotelo, Alexandre De Brebisson, Yoshua Bengio, and Aaron C Courville. 2019. Melgan: Generative adversarial networks for conditional waveform synthesis. Advances in neural information processing systems, Vol. 32 (2019).

[68]

Lei Li, Yongfeng Zhang, and Li Chen. 2021. Personalized transformer for explainable recommendation. In Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021. Association for Computational Linguistics (ACL), 4947--4957.

[69]

Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In Proceedings of the 2018 world wide web conference. 689--698.

Digital Library

[70]

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 preprint arXiv:1907.11692 (2019).

[71]

Zhuang Liu, Hanzi Mao, Chaozheng Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. 2022. A ConvNet for the 2020s. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), 11966--11976.

[72]

Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In Advances in Neural Information Processing Systems. 13--23.

[73]

Scott M Lundberg and Su-In Lee. 2017a. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems. 4765--4774.

[74]

Scott M Lundberg and Su-In Lee. 2017b. A unified approach to interpreting model predictions. Advances in neural information processing systems, Vol. 30 (2017).

[75]

Itzik Malkiel, Oren Barkan, Avi Caciularu, Noam Razin, Ori Katz, and Noam Koenigstein. 2020. RecoBERT: A catalog language model for text-based recommendations. arXiv preprint arXiv:2009.13292 (2020).

[76]

Itzik Malkiel, Dvir Ginzburg, Oren Barkan, Avi Caciularu, Jonathan Weill, and Noam Koenigstein. 2022. Interpreting BERT-based text similarity via activation and saliency maps. In Proceedings of the ACM Web Conference 2022. 3259--3268.

Digital Library

[77]

Alessandro B Melchiorre, Navid Rekabsaz, Christian Ganhör, and Markus Schedl. 2022. ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations. In Proceedings of the 16th ACM Conference on Recommender Systems. 246--256.

Digital Library

[78]

Tomas Mikolov, Wen tau Yih, and Geoffrey Zweig. 2013. Linguistic regularities in continuous space word representations. In NAACL-HLT.

[79]

Ali Modarressi, Mohsen Fayyaz, Ehsan Aghazadeh, Yadollah Yaghoobzadeh, and Mohammad Taher Pilehvar. 2023. DecompX: Explaining Transformers Decisions by Propagating Token Decomposition. arXiv preprint arXiv:2306.02873 (2023).

[80]

Caio Nóbrega and Leandro Marinho. 2019. Towards explaining recommendations through local surrogate models. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. 1671--1678.

Digital Library

[81]

Georgina Peake and Jun Wang. 2018. Explanation mining: Post hoc interpretability of latent factor models for recommendation systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2060--2069.

Digital Library

[82]

Vitali Petsiuk, Abir Das, and Kate Saenko. 2018. Rise: Randomized input sampling for explanation of black-box models. arXiv preprint arXiv:1806.07421 (2018).

[83]

Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language Models are Unsupervised Multitask Learners. (2019).

[84]

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2019. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019).

[85]

Steffen Rendle. 2021. Item recommendation from implicit feedback. In Recommender Systems Handbook. Springer, 143--171.

[86]

Steffen Rendle, Walid Khttps://www.office.com/?auth=2richene, Li Zhang, and Yehuda Koren. 2022. Revisiting the performance of ials on item recommendation benchmarks. In Proceedings of the 16th ACM Conference on Recommender Systems. 427--435.

Digital Library

[87]

Steffen Rendle, Walid Krichene, Li Zhang, and John Anderson. 2020. Neural collaborative filtering vs. matrix factorization revisited. In Fourteenth ACM conference on recommender systems. 240--248.

Digital Library

[88]

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. “Why should i trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 1135--1144.

Digital Library

[89]

Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller. 2016. Evaluating the visualization of what a deep neural network has learned. IEEE transactions on neural networks and learning systems, Vol. 28, 11 (2016), 2660--2673.

[90]

Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision. 618--626.

[91]

Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, and Sergey I Nikolenko. 2020. Recvae: A new variational autoencoder for top-n recommendations with implicit feedback. In Proceedings of the 13th international conference on web search and data mining. 528--536.

Digital Library

[92]

Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje. 2016. Not just a black box: Learning important features through propagating activation differences. arXiv preprint arXiv:1605.01713 (2016).

[93]

Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).

[94]

Ramni Harbir Singh, Sargam Maurya, Tanisha Tripathi, Tushar Narula, and Gaurav Srivastav. 2020. Movie recommendation system using cosine similarity and KNN. International Journal of Engineering and Advanced Technology, Vol. 9, 5 (2020), 556--559.

[95]

Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic attribution for deep networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 3319--3328.

Digital Library

[96]

Kirsten Swearingen and Rashmi Sinha. 2001. Beyond algorithms: An HCI perspective on recommender systems. In ACM SIGIR 2001 workshop on recommender systems, Vol. 13. 1--11.

[97]

Panagiotis Symeonidis, Alexandros Nanopoulos, and Yannis Manolopoulos. 2009. MoviExplain: a recommender system with explanations. In Proceedings of the third ACM conference on Recommender systems. 317--320.

Digital Library

[98]

Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, and Yongfeng Zhang. 2021. Counterfactual explainable recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 1784--1793.

Digital Library

[99]

Nava Tintarev and Judith Masthoff. 2022. Beyond explaining single item recommendations. Recommender Systems Handbook (2022), 711--756.

[100]

Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023).

[101]

Khanh Hiep Tran, Azin Ghazimatin, and Rishiraj Saha Roy. 2021. Counterfactual explanations for neural recommenders. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1627--1631.

Digital Library

[102]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017).

[103]

Sahil Verma, Varich Boonsanong, Minh Hoang, Keegan E Hines, John P Dickerson, and Chirag Shah. 2020. Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review. arXiv preprint arXiv:2010.10596 (2020).

[104]

Jesse Vig, Shilad Sen, and John Riedl. 2009. Tagsplanations: explaining recommendations using tags. In Proceedings of the 14th international conference on Intelligent user interfaces. 47--56.

Digital Library

[105]

Nan Wang, Hongning Wang, Yiling Jia, and Yue Yin. 2018. Explainable recommendation via multi-task learning in opinionated text data. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 165--174.

Digital Library

[106]

Peng Wang, Renqin Cai, and Hongning Wang. 2022. Graph-based Extractive Explainer for Recommendations. In Proceedings of the ACM Web Conference 2022. 2163--2171.

Digital Library

[107]

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval. 165--174.

Digital Library

[108]

Yikun Xian, Tong Zhao, Jin Li, Jim Chan, Andrey Kan, Jun Ma, Xin Luna Dong, Christos Faloutsos, George Karypis, Shan Muthukrishnan, et al. 2021. Ex3: Explainable attribute-aware item-set recommendations. In Proceedings of the 15th ACM Conference on Recommender Systems. 484--494.

Digital Library

[109]

Yongfeng Zhang, Xu Chen, et al. 2020. Explainable recommendation: A survey and new perspectives. Foundations and Trends® in Information Retrieval, Vol. 14, 1 (2020), 1--101.

Digital Library

[110]

Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. 2014. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. 83--92.

Digital Library

[111]

Jinfeng Zhong and Elsa Negre. 2022. Shap-enhanced counterfactual explanations for recommendations. In Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing. 1365--1372. n

Digital Library

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Eytan LBogina VBen-Gal IKoenigstein N(2024)KPAR: Knowledge-aware Path-based Attentive Recommender with InterpretabilityACM Transactions on Recommender Systems10.1145/3673243Online publication date: 17-Jun-2024
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Index Terms

A Counterfactual Framework for Learning and Evaluating Explanations for Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Counterfactual Explanations for Visual Recommender Systems
WWW '24: Companion Proceedings of the ACM Web Conference 2024

Users rely on clever recommendations for items they might like to buy, and service providers rely on clever recommender systems to ensure that their product is recommended to their target audience. Providing explanations for recommendations helps to ...
LLM-generated Explanations for Recommender Systems
UMAP Adjunct '24: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization

Users are often confronted with situations where they have to decide in favor or against an offered item, like a book, movie, or recipe. Those suggested items are commonly determined by a recommender system, which considers personal preferences to ...
Metrics for Evaluating Explainable Recommender Systems
Explainable and Transparent AI and Multi-Agent Systems
Abstract
Recommender systems aim to support their users by reducing information overload so that they can make better decisions. Recommender systems must be transparent, so users can form mental models about the system’s goals, internal state, and ...

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Published In

cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

Copyright © 2024 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2024

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Israeli Science Foundation (ISF)

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WWW '24

Sponsor:

SIGWEB

WWW '24: The ACM Web Conference 2024

May 13 - 17, 2024

Singapore, Singapore

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Eytan LBogina VBen-Gal IKoenigstein N(2024)KPAR: Knowledge-aware Path-based Attentive Recommender with InterpretabilityACM Transactions on Recommender Systems10.1145/3673243Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3673243
Mohammadi APeintner AMüller MZangerle E(2024)Are We Explaining the Same Recommenders? Incorporating Recommender Performance for Evaluating ExplainersProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691709(1113-1118)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691709
Baklanov M(2024)CEERS: Counterfactual Evaluations of Explanations in Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688015(1323-1329)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688015
Elisha YBarkan OKoenigstein NSerra ESpezzano F(2024)Probabilistic Path Integration with Mixture of Baseline DistributionsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679641(570-580)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679641

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