{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T06:31:19Z","timestamp":1751524279580,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031887109","type":"print"},{"value":"9783031887116","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-88711-6_4","type":"book-chapter","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T17:15:33Z","timestamp":1743786933000},"page":"55-71","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Feature Attribution Explanations of\u00a0Session-Based Recommendations"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1619-4076","authenticated-orcid":false,"given":"Simone","family":"Borg Bruun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7001-4817","authenticated-orcid":false,"given":"Maria","family":"Maistro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2600-2701","authenticated-orcid":false,"given":"Christina","family":"Lioma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,4]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","unstructured":"Aas, K., Jullum, M., L\u00f8land, A.: Explaining individual predictions when features are dependent: more accurate approximations to shapley values. Artif. Intell. 298, 103502 (2021). https:\/\/doi.org\/10.1016\/j.artint.2021.103502","DOI":"10.1016\/j.artint.2021.103502"},{"key":"4_CR2","unstructured":"Agarwal, C., et al.: Rethinking stability for attribution-based explanations. In: ICLR 2022 Workshop on PAIR$$^2$$Struct: Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data (2022)"},{"key":"4_CR3","unstructured":"Agarwal, C., et al.: OpenXAI: towards a transparent evaluation of model explanations. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems, vol.\u00a035, pp. 15784\u201315799. Curran Associates, Inc. (2022). https:\/\/papers.nips.cc\/paper_files\/paper\/2022\/hash\/65398a0eba88c9b4a1c38ae405b125ef-Abstract-Datasets_and_Benchmarks.html"},{"key":"4_CR4","unstructured":"Alvarez-Melis, D., Jaakkola, T.S.: Towards robust interpretability with self-explaining neural networks. In: Bengio, S., Wallach, H.M., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, pp. 7786\u20137795 (2018). https:\/\/proceedings.neurips.cc\/paper\/2018\/hash\/3e9f0fc9b2f89e043bc6233994dfcf76-Abstract.html"},{"key":"4_CR5","doi-asserted-by":"publisher","unstructured":"Atanasova, P., Simonsen, J.G., Lioma, C., Augenstein, I.: Generating fact checking explanations. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, pp. 7352\u20137364. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.656","DOI":"10.18653\/v1\/2020.acl-main.656"},{"key":"4_CR6","doi-asserted-by":"publisher","unstructured":"Bhatt, U., Weller, A., Moura, J.M.F.: Evaluating and aggregating feature-based model explanations. In: Bessiere, C. (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pp. 3016\u20133022. International Joint Conferences on Artificial Intelligence Organization (2020). https:\/\/doi.org\/10.24963\/ijcai.2020\/417","DOI":"10.24963\/ijcai.2020\/417"},{"key":"4_CR7","doi-asserted-by":"publisher","unstructured":"Brunot, L., Canovas, N., Chanson, A., Labroche, N., Verdeaux, W.: Preference-based and local post-hoc explanations for recommender systems. Inf. Syst. 108, 102021 (2022). https:\/\/doi.org\/10.1016\/j.is.2022.102021","DOI":"10.1016\/j.is.2022.102021"},{"key":"4_CR8","doi-asserted-by":"publisher","unstructured":"Chen, J., Wu, W., Hu, W., Zheng, W., He, L.: SSR: explainable session-based recommendation. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20138 (2021). https:\/\/doi.org\/10.1109\/IJCNN52387.2021.9534196","DOI":"10.1109\/IJCNN52387.2021.9534196"},{"key":"4_CR9","doi-asserted-by":"publisher","unstructured":"Davidson, J., et al.: The YouTube video recommendation system. In: Amatriain, X., Torrens, M., Resnick, P., Zanker, M. (eds.) Proceedings of the 4th ACM Conference on Recommender Systems, (RecSys 2010), pp. 293\u2013296. ACM (2010). https:\/\/doi.org\/10.1145\/1864708.1864770","DOI":"10.1145\/1864708.1864770"},{"key":"4_CR10","unstructured":"Dervishaj, E., Ruotsalo, T., Maistro, M., Lioma, C.: Are representation disentanglement and interpretability linked in recommendation models? A critical review and reproducibility study. In: Proceeding of the 47th European Conference on Information Retrieval, ECIR 2025. Springer, Cham (2025, in press)"},{"key":"4_CR11","doi-asserted-by":"publisher","unstructured":"DeYoung, J., et al.: ERASER: a benchmark to evaluate rationalized NLP models. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, pp. 4443\u20134458. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.408","DOI":"10.18653\/v1\/2020.acl-main.408"},{"key":"4_CR12","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016 (2016)"},{"key":"4_CR13","doi-asserted-by":"publisher","unstructured":"Hu, H., He, X., Gao, J., Zhang, Z.L.: Modeling personalized item frequency information for next-basket recommendation. In: Huang, J., Chang, Y., Cheng, X., Kamps, J., Murdock, V., Wen, J.R., Liu, Y. (eds.) Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, (SIGIR 2020), pp. 1071\u20131080. ACM (2020). https:\/\/doi.org\/10.1145\/3397271.3401066","DOI":"10.1145\/3397271.3401066"},{"key":"4_CR14","doi-asserted-by":"publisher","unstructured":"Iferroudjene, M., Lonjarret, C., Robardet, C., Plantevit, M., Atzmueller, M.: Methods for explaining top-N recommendations through subgroup discovery. Data Mining Knowl. Discov. 37, 833\u2013872 (2022). https:\/\/doi.org\/10.1007\/s10618-022-00897-2","DOI":"10.1007\/s10618-022-00897-2"},{"key":"4_CR15","doi-asserted-by":"publisher","unstructured":"Jacovi, A., Goldberg, Y.: Towards faithfully interpretable NLP systems: how should we define and evaluate faithfulness? In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, pp. 4198\u20134205. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.386","DOI":"10.18653\/v1\/2020.acl-main.386"},{"key":"4_CR16","doi-asserted-by":"publisher","unstructured":"Jain, S., Wiegreffe, S., Pinter, Y., Wallace, B.C.: Learning to faithfully rationalize by construction. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, pp. 4459\u20134473. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.409","DOI":"10.18653\/v1\/2020.acl-main.409"},{"key":"4_CR17","doi-asserted-by":"publisher","unstructured":"Jannach, D., Ludewig, M.: When recurrent neural networks meet the neighborhood for session-based recommendation. In: Cremonesi, P., Ricci, F., Berkovsky, S., Tuzhilin, A. (eds.) Proceedings of the 11th ACM Conference on Recommender Systems, (RecSys 2017), pp. 306\u2013310. ACM (2017). https:\/\/doi.org\/10.1145\/3109859.3109872","DOI":"10.1145\/3109859.3109872"},{"key":"4_CR18","doi-asserted-by":"publisher","unstructured":"Jiang, G., et al.: SeqSHAP: subsequence level shapley value explanations for sequential predictions. In: Onizuka, M., et al. (eds.) Database Systems for Advanced Applications - 29th International Conference, DASFAA 2024, Proceedings, Part IV. Lecture Notes in Computer Science, vol. 14853, pp. 89\u2013104. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-981-97-5562-2_6","DOI":"10.1007\/978-981-97-5562-2_6"},{"key":"4_CR19","doi-asserted-by":"publisher","unstructured":"Kang, W.C., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 197\u2013206. IEEE Computer Society, Los Alamitos, CA, USA (2018).https:\/\/doi.org\/10.1109\/ICDM.2018.00035","DOI":"10.1109\/ICDM.2018.00035"},{"key":"4_CR20","unstructured":"Kutner, M., Nachtsheim, C.J., Neter, J., Li, W., et al.: Applied Linear Statistical Models. McGraw-Hill, Irwin (2005)"},{"key":"4_CR21","doi-asserted-by":"publisher","unstructured":"Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, pp. 1419\u20131428. Association for Computing Machinery, New York (2017). https:\/\/doi.org\/10.1145\/3132847.3132926","DOI":"10.1145\/3132847.3132926"},{"key":"4_CR22","doi-asserted-by":"publisher","unstructured":"Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76\u201380 (2003). https:\/\/doi.org\/10.1109\/MIC.2003.1167344","DOI":"10.1109\/MIC.2003.1167344"},{"key":"4_CR23","doi-asserted-by":"publisher","unstructured":"Lonjarret, C., Robardet, C., Plantevit, M., Auburtin, R., Atzmueller, M.: Why should i trust this item? Explaining the recommendations of any model. In: 7th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2020, pp. 526\u2013535. IEEE (2020). https:\/\/doi.org\/10.1109\/DSAA49011.2020.00067","DOI":"10.1109\/DSAA49011.2020.00067"},{"key":"4_CR24","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, pp. 4768\u20134777. Curran Associates Inc., Red Hook (2017). https:\/\/papers.nips.cc\/paper_files\/paper\/2017\/hash\/8a20a8621978632d76c43dfd28b67767-Abstract.html"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Lyu, Q., Apidianaki, M., Callison-Burch, C.: Towards faithful model explanation in NLP: a survey. Comput. Linguist. 50(2), 657\u2013723 (2024). https:\/\/aclanthology.org\/2024.cl-2.6\/","DOI":"10.1162\/coli_a_00511"},{"key":"4_CR26","doi-asserted-by":"publisher","unstructured":"N\u00f3brega, C., Marinho, L.: Towards explaining recommendations through local surrogate models. In: Proceedings of the 34th ACM\/SIGAPP Symposium on Applied Computing, SAC 2019, pp. 1671\u20131678. ACM, New York (2019). https:\/\/doi.org\/10.1145\/3297280.3297443","DOI":"10.1145\/3297280.3297443"},{"key":"4_CR27","unstructured":"Petsiuk, V., Das, A., Saenko, K.: RISE: randomized input sampling for explanation of black-box models. In: British Machine Vision Conference 2018, BMVC 2018, p.\u00a0151. BMVA Press (2018). http:\/\/bmvc2018.org\/contents\/papers\/1064.pdf"},{"key":"4_CR28","unstructured":"Queen, O., Hartvigsen, T., Koker, T., He, H., Tsiligkaridis, T., Zitnik, M.: Encoding time-series explanations through self-supervised model behavior consistency. In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023 (2023). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2023\/hash\/65ea878cb90b440e8b4cd34fe0959914-Abstract-Conference.html"},{"key":"4_CR29","doi-asserted-by":"publisher","unstructured":"Ribeiro, M., Singh, S., Guestrin, C.: \u201cWhy should i trust you?\u201d: explaining the predictions of any classifier. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, San Diego, California, pp. 97\u2013101. Association for Computational Linguistics (2016). https:\/\/doi.org\/10.18653\/v1\/N16-3020","DOI":"10.18653\/v1\/N16-3020"},{"key":"4_CR30","doi-asserted-by":"publisher","unstructured":"Roberts, C., Elahi, E., Chandrashekar, A.: CLIME: completeness-constrained LIME. In: Companion Proceedings of the ACM Web Conference 2023, WWW 2023 Companion, pp. 950\u2013958. Association for Computing Machinery, New York (2023). https:\/\/doi.org\/10.1145\/3543873.3587652","DOI":"10.1145\/3543873.3587652"},{"key":"4_CR31","doi-asserted-by":"publisher","unstructured":"Strumbelj, E., Kononenko, I.: Explaining prediction models and individual predictions with feature contributions. Knowl. Inf. Syst. 41(3), 647\u2013665 (2014). https:\/\/doi.org\/10.1007\/s10115-013-0679-x","DOI":"10.1007\/s10115-013-0679-x"},{"key":"4_CR32","doi-asserted-by":"publisher","unstructured":"Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., Jiang, P.: BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, pp. 1441\u20131450. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3357384.3357895","DOI":"10.1145\/3357384.3357895"},{"key":"4_CR33","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017. Proceedings of Machine Learning Research, vol.\u00a070, pp. 3319\u20133328. PMLR (2017). http:\/\/proceedings.mlr.press\/v70\/sundararajan17a.html"},{"key":"4_CR34","unstructured":"Turrin, R., Quadrana, M., Condorelli, A., Pagano, R., Cremonesi, P.: 30Music listening and playlists dataset. In: Castells, P. (ed.) Poster Proceedings of the 9th ACM Conference on Recommender Systems, RecSys 2015. CEUR Workshop Proceedings, vol.\u00a01441. CEUR-WS.org (2015). https:\/\/ceur-ws.org\/Vol-1441\/recsys2015_poster13.pdf"},{"key":"4_CR35","doi-asserted-by":"publisher","unstructured":"Wang, S., Cao, L., Wang, Y., Sheng, Q.Z., Orgun, M.A., Lian, D.: A survey on session-based recommender systems. ACM Comput. Surv. 54(7), 154:1\u2013154:38 (2022). https:\/\/doi.org\/10.1145\/3465401","DOI":"10.1145\/3465401"},{"key":"4_CR36","doi-asserted-by":"publisher","unstructured":"Wiegreffe, S., Marasovic, A., Smith, N.A.: Measuring association between labels and free-text rationales. In: Moens, M., Huang, X., Specia, L., Yih, S.W. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, pp. 10266\u201310284. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.804","DOI":"10.18653\/v1\/2021.emnlp-main.804"},{"key":"4_CR37","doi-asserted-by":"publisher","unstructured":"Wu, H., Geng, C., Fang, H.: Causality and correlation graph modeling for effective and explainable session-based recommendation. ACM Trans. Web 18(1) (2023). https:\/\/doi.org\/10.1145\/3593313","DOI":"10.1145\/3593313"},{"key":"4_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"},{"key":"4_CR39","doi-asserted-by":"publisher","unstructured":"Zheng, J., Mai, J., Wen, Y.: Explainable session-based recommendation with meta-path guided instances and self-attention mechanism. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022, pp. 2555\u20132559. ACM, New York (2022). https:\/\/doi.org\/10.1145\/3477495.3531895","DOI":"10.1145\/3477495.3531895"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-88711-6_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T17:15:58Z","timestamp":1743786958000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-88711-6_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031887109","9783031887116"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-88711-6_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"4 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lucca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"47","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2025.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}