Benchmarking Session-based and Session-aware Recommender Systems for Jusbrasil
Resumo
In this paper, we present a benchmark of several session-based, session-based with reminders and session-aware recommender systems that can be used to improve legal document recommendation in Jusbrasil, the largest legal search engine in Brazil. We focus this benchmark on the logged users, and the results show that some recommender systems can achieve gains of accuracy of around 19% with respect to the current recommender system adopted by Jusbrasil.
Palavras-chave:
legal document recommendation, session-based recommender systems, reminders, session-aware recommender systems
Referências
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Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016. Parallel Recurrent Neural Network Architectures for Feature-Rich Session-Based Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (Boston, Massachusetts, USA) (RecSys ’16). Association for Computing Machinery, New York, NY, USA, 241–248. https://doi.org/10.1145/2959100.2959167
Liang Hu, Qingkui Chen, Haiyan Zhao, Songlei Jian, Longbing Cao, and Jian Cao. 2018. Neural Cross-Session Filtering: Next-Item Prediction Under Intra- and Inter-Session Context. IEEE Intelligent Systems 33, 6 (2018), 57–67. https://doi.org/10.1109/MIS.2018.2881516
Dietmar Jannach, Lukas Lerche, Iman Kamehkhosh, and Michael Jugovac. 2015. What Recommenders Recommend: An Analysis of Recommendation Biases and Possible Countermeasures. User Modeling and User-Adapted Interaction 25, 5 (2015), 427–491. https://doi.org/10.1007/s11257-015-9165-3
Dietmar Jannach and Malte Ludewig. 2017. When Recurrent Neural Networks Meet the Neighborhood for Session-Based Recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems (Como, Italy) (RecSys ’17). Association for Computing Machinery, New York, NY, USA, 306–310. https://doi.org/10.1145/3109859.3109872
Dietmar Jannach, Malte Ludewig, and Lukas Lerche. 2017. Sessionbased item recommendation in e-commerce: on short-term intents, reminders, trends and discounts. User Model. User Adapt. Interact. 27, 3-5 (2017), 351–392. https://doi.org/10.1007/s11257-017-9194-1
Santosh Kabbur, Xia Ning, and George Karypis. 2013. FISM: Factored Item Similarity Models for Top-N Recommender Systems. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Chicago, Illinois, USA) (KDD ’13). Association for Computing Machinery, New York, NY, USA, 659–667. https://doi.org/10.1145/2487575.2487589
Iman Kamehkhosh, D. Jannach, and Malte Ludewig. 2017. A Comparison of Frequent Pattern Techniques and a Deep Learning Method for Session-Based Recommendation. In RecTemp@RecSys.
Sara Latifi, Noemi Mauro, and Dietmar Jannach. 2021. Session-aware recommendation: A surprising quest for the state-of-the-art. Information Sciences 573 (2021), 291–315. https://doi.org/10.1016/j.ins.2021.05.048
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural Attentive Session-Based Recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (Singapore, Singapore) (CIKM ’17). Association for Computing Machinery, New York, NY, USA, 1419–1428. https://doi.org/10.1145/3132847.3132926
Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018. STAMP: Short-Term Attention/Memory Priority Model for Session-Based Recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (London, United Kingdom) (KDD ’18). Association for Computing Machinery, New York, NY, USA, 1831–1839. https://doi.org/10.1145/3219819.3219950
Malte Ludewig and Dietmar Jannach. 2018. Evaluation of Session-Based Recommendation Algorithms. User Modeling and User-Adapted Interaction 28, 4–5 (2018), 331–390. https://doi.org/10.1007/s11257-018-9209-6
Malte Ludewig, Noemi Mauro, Sara Latifi, and Dietmar Jannach. 2021. Empirical analysis of session-based recommendation algorithms. User Model. User Adapt. Interact. 31, 1 (2021), 149–181. https://doi.org/10.1007/s11257-020-09277-1
Edleno Moura, Rafael Costa, Gabriel Jordão, and Gustavo Maia. 2021. Jusbrasil e os Desafios Tecnológicos para Facilitar e Aprimorar o Acesso à Justiça. In Anais do XLVIII Seminário Integrado de Software e Hardware (Evento Online). SBC, Porto Alegre, RS, Brasil, 207–213. https://doi.org/10.5753/semish.2021.15824
J. R. Norris. 1997. Markov Chains. Cambridge University Press. https://doi.org/10.1017/CBO9780511810633
Tu Minh Phuong, Tran Cong Thanh, and Ngo Xuan Bach. 2019. Neural Session-Aware Recommendation. IEEE Access 7 (2019), 86884–86896. (2019), 86884–86896. https://doi.org/10.1109/ACCESS.2019.2926074
Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. 2017. Personalizing Session-Based Recommendations with Hierarchical Recurrent Neural Networks. In Proceedings of the Eleventh ACM Conference on Recommender Systems (Como, Italy) (RecSys ’17). Association for Computing Machinery, New York, NY, USA, 130–137. https://doi.org/10.1145/3109859.3109896
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing Personalized Markov Chains for Next-Basket Recommendation. In Proceedings of the 19th International Conference on World Wide Web (Raleigh, North Carolina, USA) (WWW ’10). Association for Computing Machinery, New York, NY, USA, 811–820. https://doi.org/10.1145/1772690.1772773
Francesco Ricci, Lior Rokach, Bracha Shapira, and Paul B. Kantor (Eds.). 2011. Recommender Systems Handbook. Springer. https://doi.org/10.1007/978-0-387-85820-3
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-Based Recommendation with Graph Neural Networks. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence (Honolulu, Hawaii, USA) (AAAI’19/IAAI’19/EAAI’19). AAAI Press, Article 43, 8 pages. https://doi.org/10.1609/aaai.v33i01.3301346
Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, Yanchi Liu, Guandong Xu, Xing Xie, Hui Xiong, and Jian Wu. 2018. Sequential Recommender System based on Hierarchical Attention Networks. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18. International Joint Conferences on Artificial Intelligence Organization, 3926–3932. https://doi.org/10.24963/ijcai.2018/546
Robert Cooley, Bamshad Mobasher, and Jaideep Srivastava. 1999. Data Preparation for Mining World Wide Web Browsing Patterns. Knowl. Inf. Syst. 1, 1 (1999), 5–32. https://doi.org/10.1007/BF03325089
Diksha Garg, Priyanka Gupta, Pankaj Malhotra, Lovekesh Vig, and Gautam Shroff. 2019. Sequence and Time Aware Neighborhood for Session-Based Recommendations: STAN. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris, France) (SIGIR’19). Association for Computing Machinery, New York, NY, USA, 1069–1072. https://doi.org/10.1145/3331184.3331322
Ruining He and Julian McAuley. 2016. Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation. In 2016 IEEE 16th International Conference on Data Mining (ICDM). 191–200. https://doi.org/10.1109/ICDM.2016.0030
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based Recommendations with Recurrent Neural Networks. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1511.06939
Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016. Parallel Recurrent Neural Network Architectures for Feature-Rich Session-Based Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (Boston, Massachusetts, USA) (RecSys ’16). Association for Computing Machinery, New York, NY, USA, 241–248. https://doi.org/10.1145/2959100.2959167
Liang Hu, Qingkui Chen, Haiyan Zhao, Songlei Jian, Longbing Cao, and Jian Cao. 2018. Neural Cross-Session Filtering: Next-Item Prediction Under Intra- and Inter-Session Context. IEEE Intelligent Systems 33, 6 (2018), 57–67. https://doi.org/10.1109/MIS.2018.2881516
Dietmar Jannach, Lukas Lerche, Iman Kamehkhosh, and Michael Jugovac. 2015. What Recommenders Recommend: An Analysis of Recommendation Biases and Possible Countermeasures. User Modeling and User-Adapted Interaction 25, 5 (2015), 427–491. https://doi.org/10.1007/s11257-015-9165-3
Dietmar Jannach and Malte Ludewig. 2017. When Recurrent Neural Networks Meet the Neighborhood for Session-Based Recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems (Como, Italy) (RecSys ’17). Association for Computing Machinery, New York, NY, USA, 306–310. https://doi.org/10.1145/3109859.3109872
Dietmar Jannach, Malte Ludewig, and Lukas Lerche. 2017. Sessionbased item recommendation in e-commerce: on short-term intents, reminders, trends and discounts. User Model. User Adapt. Interact. 27, 3-5 (2017), 351–392. https://doi.org/10.1007/s11257-017-9194-1
Santosh Kabbur, Xia Ning, and George Karypis. 2013. FISM: Factored Item Similarity Models for Top-N Recommender Systems. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Chicago, Illinois, USA) (KDD ’13). Association for Computing Machinery, New York, NY, USA, 659–667. https://doi.org/10.1145/2487575.2487589
Iman Kamehkhosh, D. Jannach, and Malte Ludewig. 2017. A Comparison of Frequent Pattern Techniques and a Deep Learning Method for Session-Based Recommendation. In RecTemp@RecSys.
Sara Latifi, Noemi Mauro, and Dietmar Jannach. 2021. Session-aware recommendation: A surprising quest for the state-of-the-art. Information Sciences 573 (2021), 291–315. https://doi.org/10.1016/j.ins.2021.05.048
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural Attentive Session-Based Recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (Singapore, Singapore) (CIKM ’17). Association for Computing Machinery, New York, NY, USA, 1419–1428. https://doi.org/10.1145/3132847.3132926
Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018. STAMP: Short-Term Attention/Memory Priority Model for Session-Based Recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (London, United Kingdom) (KDD ’18). Association for Computing Machinery, New York, NY, USA, 1831–1839. https://doi.org/10.1145/3219819.3219950
Malte Ludewig and Dietmar Jannach. 2018. Evaluation of Session-Based Recommendation Algorithms. User Modeling and User-Adapted Interaction 28, 4–5 (2018), 331–390. https://doi.org/10.1007/s11257-018-9209-6
Malte Ludewig, Noemi Mauro, Sara Latifi, and Dietmar Jannach. 2021. Empirical analysis of session-based recommendation algorithms. User Model. User Adapt. Interact. 31, 1 (2021), 149–181. https://doi.org/10.1007/s11257-020-09277-1
Edleno Moura, Rafael Costa, Gabriel Jordão, and Gustavo Maia. 2021. Jusbrasil e os Desafios Tecnológicos para Facilitar e Aprimorar o Acesso à Justiça. In Anais do XLVIII Seminário Integrado de Software e Hardware (Evento Online). SBC, Porto Alegre, RS, Brasil, 207–213. https://doi.org/10.5753/semish.2021.15824
J. R. Norris. 1997. Markov Chains. Cambridge University Press. https://doi.org/10.1017/CBO9780511810633
Tu Minh Phuong, Tran Cong Thanh, and Ngo Xuan Bach. 2019. Neural Session-Aware Recommendation. IEEE Access 7 (2019), 86884–86896. (2019), 86884–86896. https://doi.org/10.1109/ACCESS.2019.2926074
Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. 2017. Personalizing Session-Based Recommendations with Hierarchical Recurrent Neural Networks. In Proceedings of the Eleventh ACM Conference on Recommender Systems (Como, Italy) (RecSys ’17). Association for Computing Machinery, New York, NY, USA, 130–137. https://doi.org/10.1145/3109859.3109896
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing Personalized Markov Chains for Next-Basket Recommendation. In Proceedings of the 19th International Conference on World Wide Web (Raleigh, North Carolina, USA) (WWW ’10). Association for Computing Machinery, New York, NY, USA, 811–820. https://doi.org/10.1145/1772690.1772773
Francesco Ricci, Lior Rokach, Bracha Shapira, and Paul B. Kantor (Eds.). 2011. Recommender Systems Handbook. Springer. https://doi.org/10.1007/978-0-387-85820-3
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-Based Recommendation with Graph Neural Networks. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence (Honolulu, Hawaii, USA) (AAAI’19/IAAI’19/EAAI’19). AAAI Press, Article 43, 8 pages. https://doi.org/10.1609/aaai.v33i01.3301346
Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, Yanchi Liu, Guandong Xu, Xing Xie, Hui Xiong, and Jian Wu. 2018. Sequential Recommender System based on Hierarchical Attention Networks. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18. International Joint Conferences on Artificial Intelligence Organization, 3926–3932. https://doi.org/10.24963/ijcai.2018/546
Publicado
07/11/2022
Como Citar
DOMINGUES, Marcos Aurélio; MOURA, Edleno Silva de; MARINHO, Leandro Balby; SILVA, Altigran da.
Benchmarking Session-based and Session-aware Recommender Systems for Jusbrasil. In: WEBMEDIA IN PRACTICE - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 28. , 2022, Curitiba.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 145-148.
ISSN 2596-1683.
DOI: https://doi.org/10.5753/webmedia_estendido.2022.WiP02.
