Recommender system (English Wikipedia)

Felfernig, Alexander; Isak, Klaus; Szabo, Kalman; Zachar, Peter (2007). "The VITA Financial Services Sales Support Environment" (PDF). In William Cheetham (ed.). Proceedings of the 19th National Conference on Innovative Applications of Artificial Intelligence, vol. 2. pp. 1692–1699. ISBN 9781577353232. ACM Copy.

academia.edu

Lathia, N., Hailes, S., Capra, L., Amatriain, X.: Temporal diversity in recommender systems^{[dead link‍]}. In: Proceedings of the 33rd International ACMSIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 210–217. ACM, New York

acm.org

dl.acm.org

Felfernig, Alexander; Isak, Klaus; Szabo, Kalman; Zachar, Peter (2007). "The VITA Financial Services Sales Support Environment" (PDF). In William Cheetham (ed.). Proceedings of the 19th National Conference on Innovative Applications of Artificial Intelligence, vol. 2. pp. 1692–1699. ISBN 9781577353232. ACM Copy.
Vente, Tobias; Ekstrand, Michael; Beel, Joeran (September 14, 2023). "Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit". Proceedings of the 17th ACM Conference on Recommender Systems. ACM. pp. 1212–1216. doi:10.1145/3604915.3610656. ISBN 979-8-4007-0241-9.
Zhao, Wayne Xin; Mu, Shanlei; Hou, Yupeng; Lin, Zihan; Chen, Yushuo; Pan, Xingyu; Li, Kaiyuan; Lu, Yujie; Wang, Hui; Tian, Changxin; Min, Yingqian; Feng, Zhichao; Fan, Xinyan; Chen, Xu; Wang, Pengfei (October 26, 2021). "RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms". Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM. pp. 4653–4664. arXiv:2011.01731. doi:10.1145/3459637.3482016. ISBN 978-1-4503-8446-9.
Li, Jiayu; Li, Hanyu; He, Zhiyu; Ma, Weizhi; Sun, Peijie; Zhang, Min; Ma, Shaoping (October 8, 2024). "ReChorus2.0: A Modular and Task-Flexible Recommendation Library". 18th ACM Conference on Recommender Systems. ACM. pp. 454–464. doi:10.1145/3640457.3688076. ISBN 979-8-4007-0505-2.
Michiels, Lien; Verachtert, Robin; Goethals, Bart (September 18, 2022). "RecPack: An(other) Experimentation Toolkit for Top-N Recommendation using Implicit Feedback Data". Proceedings of the 16th ACM Conference on Recommender Systems. ACM. pp. 648–651. doi:10.1145/3523227.3551472. ISBN 978-1-4503-9278-5.
Zou, Lixin; Xia, Long; Ding, Zhuoye; Song, Jiaxing; Liu, Weidong; Yin, Dawei (2019). "Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems". Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD '19. pp. 2810–2818. arXiv:1902.05570. doi:10.1145/3292500.3330668. ISBN 978-1-4503-6201-6. S2CID 62903207.
Ferrari Dacrema, Maurizio; Boglio, Simone; Cremonesi, Paolo; Jannach, Dietmar (January 8, 2021). "A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research". ACM Transactions on Information Systems. 39 (2): 1–49. arXiv:1911.07698. doi:10.1145/3434185. hdl:11311/1164333. S2CID 208138060.
Ferrari Dacrema, Maurizio; Cremonesi, Paolo; Jannach, Dietmar (2019). "Are we really making much progress? A worrying analysis of recent neural recommendation approaches". Proceedings of the 13th ACM Conference on Recommender Systems. RecSys '19. ACM. pp. 101–109. arXiv:1907.06902. doi:10.1145/3298689.3347058. hdl:11311/1108996. ISBN 978-1-4503-6243-6. S2CID 196831663. Retrieved October 16, 2019.
Sun, Zhu; Yu, Di; Fang, Hui; Yang, Jie; Qu, Xinghua; Zhang, Jie; Geng, Cong (2020). "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison". Fourteenth ACM Conference on Recommender Systems. ACM. pp. 23–32. doi:10.1145/3383313.3412489. ISBN 978-1-4503-7583-2. S2CID 221785064.
Volkovs, Maksims; Rai, Himanshu; Cheng, Zhaoyue; Wu, Ga; Lu, Yichao; Sanner, Scott (2018). "Two-stage Model for Automatic Playlist Continuation at Scale". Proceedings of the ACM Recommender Systems Challenge 2018. ACM. pp. 1–6. doi:10.1145/3267471.3267480. ISBN 978-1-4503-6586-4. S2CID 52942462.

portal.acm.org

Adomavicius, G.; Tuzhilin, A. (June 2005). "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions". IEEE Transactions on Knowledge and Data Engineering. 17 (6): 734–749. CiteSeerX 10.1.1.107.2790. doi:10.1109/TKDE.2005.99. S2CID 206742345..

american.edu

digitalcommons.wcl.american.edu

Mirkin, Sima (June 4, 2014). ""Extending and Customizing Content Discovery for the Legal Academic Com" by Sima Mirkin". Articles in Law Reviews & Other Academic Journals. Digital Commons @ American University Washington College of Law. Retrieved December 31, 2015.

ams.org

blogs.ams.org

Blanda, Stephanie (May 25, 2015). "Online Recommender Systems – How Does a Website Know What I Want?". American Mathematical Society. Retrieved October 31, 2016.

archive.org

Andrew I. Schein; Alexandrin Popescul; Lyle H. Ungar; David M. Pennock (2002). Methods and Metrics for Cold-Start Recommendations. Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002). : ACM. pp. 253–260. ISBN 1-58113-561-0. Retrieved February 2, 2008.
Peter Brusilovsky (2007). The Adaptive Web. Springer. p. 325. ISBN 978-3-540-72078-2.
Jannach, Dietmar; Lerche, Lukas; Gedikli, Fatih; Bonnin, Geoffray (June 10, 2013). "What Recommenders Recommend – an Analysis of Accuracy, Popularity, and Sales Diversity Effects". In Carberry, Sandra; Weibelzahl, Stephan; Micarelli, Alessandro; Semeraro, Giovanni (eds.). User Modeling, Adaptation, and Personalization. Lecture Notes in Computer Science. Vol. 7899. Springer Berlin Heidelberg. pp. 25–37. CiteSeerX 10.1.1.465.96. doi:10.1007/978-3-642-38844-6_3. ISBN 978-3-642-38843-9.
Turpin, Andrew H.; Hersh, William (January 1, 2001). "Why batch and user evaluations do not give the same results". Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval. SIGIR '01. New York, NY, USA: ACM. pp. 225–231. CiteSeerX 10.1.1.165.5800. doi:10.1145/383952.383992. ISBN 978-1-58113-331-8. S2CID 18903114.
Naren Ramakrishnan; Benjamin J. Keller; Batul J. Mirza; Ananth Y. Grama; George Karypis (2001). "Privacy risks in recommender systems". IEEE Internet Computing. 5 (6). Piscataway, NJ: IEEE Educational Activities Department: 54–62. CiteSeerX 10.1.1.2.2932. doi:10.1109/4236.968832. ISBN 978-1-58113-561-9. S2CID 1977107.

archives-ouvertes.fr

tel.archives-ouvertes.fr

Bouneffouf, Djallel (2013), DRARS, A Dynamic Risk-Aware Recommender System (Ph.D.), Institut National des Télécommunications

arxiv.org

H. Chen, A. G. Ororbia II, C. L. Giles ExpertSeer: a Keyphrase Based Expert Recommender for Digital Libraries, in arXiv preprint 2015
Zhao, Wayne Xin; Mu, Shanlei; Hou, Yupeng; Lin, Zihan; Chen, Yushuo; Pan, Xingyu; Li, Kaiyuan; Lu, Yujie; Wang, Hui; Tian, Changxin; Min, Yingqian; Feng, Zhichao; Fan, Xinyan; Chen, Xu; Wang, Pengfei (October 26, 2021). "RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms". Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM. pp. 4653–4664. arXiv:2011.01731. doi:10.1145/3459637.3482016. ISBN 978-1-4503-8446-9.
John S. Breese; David Heckerman & Carl Kadie (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence (UAI'98). arXiv:1301.7363.
Hidasi, Balázs; Karatzoglou, Alexandros; Baltrunas, Linas; Tikk, Domonkos (March 29, 2016). "Session-based Recommendations with Recurrent Neural Networks". arXiv:1511.06939 [cs.LG].
Chen, Minmin; Beutel, Alex; Covington, Paul; Jain, Sagar; Belletti, Francois; Chi, Ed (2018). "Top-K Off-Policy Correction for a REINFORCE Recommender System". arXiv:1812.02353 [cs.LG].
Hidasi, Balázs; Karatzoglou, Alexandros (October 17, 2018). "Recurrent Neural Networks with Top-k Gains for Session-based Recommendations". Proceedings of the 27th ACM International Conference on Information and Knowledge Management. CIKM '18. Torino, Italy: Association for Computing Machinery. pp. 843–852. arXiv:1706.03847. doi:10.1145/3269206.3271761. ISBN 978-1-4503-6014-2. S2CID 1159769.
Kang, Wang-Cheng; McAuley, Julian (2018). "Self-Attentive Sequential Recommendation". arXiv:1808.09781 [cs.IR].
Li, Jing; Ren, Pengjie; Chen, Zhumin; Ren, Zhaochun; Lian, Tao; Ma, Jun (November 6, 2017). "Neural Attentive Session-based Recommendation". Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. CIKM '17. Singapore, Singapore: Association for Computing Machinery. pp. 1419–1428. arXiv:1711.04725. doi:10.1145/3132847.3132926. ISBN 978-1-4503-4918-5. S2CID 21066930.
Xin, Xin; Karatzoglou, Alexandros; Arapakis, Ioannis; Jose, Joemon (2020). "Self-Supervised Reinforcement Learning for Recommender Systems". arXiv:2006.05779 [cs.LG].
Zou, Lixin; Xia, Long; Ding, Zhuoye; Song, Jiaxing; Liu, Weidong; Yin, Dawei (2019). "Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems". Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD '19. pp. 2810–2818. arXiv:1902.05570. doi:10.1145/3292500.3330668. ISBN 978-1-4503-6201-6. S2CID 62903207.
Pimenidis, Elias; Polatidis, Nikolaos; Mouratidis, Haralambos (August 3, 2018). "Mobile recommender systems: Identifying the major concepts". Journal of Information Science. 45 (3): 387–397. arXiv:1805.02276. doi:10.1177/0165551518792213. S2CID 19209845.
Zhai, Jiaqi; Liao, Lucy; Liu, Xing; Wang, Yueming; Li, Rui; Cao, Xuan; Gao, Leon; Gong, Zhaojie; Gu, Fangda (May 6, 2024), Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations, doi:10.48550/arXiv.2402.17152, retrieved December 20, 2024
Ferrari Dacrema, Maurizio; Boglio, Simone; Cremonesi, Paolo; Jannach, Dietmar (January 8, 2021). "A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research". ACM Transactions on Information Systems. 39 (2): 1–49. arXiv:1911.07698. doi:10.1145/3434185. hdl:11311/1164333. S2CID 208138060.
Ferrari Dacrema, Maurizio; Cremonesi, Paolo; Jannach, Dietmar (2019). "Are we really making much progress? A worrying analysis of recent neural recommendation approaches". Proceedings of the 13th ACM Conference on Recommender Systems. RecSys '19. ACM. pp. 101–109. arXiv:1907.06902. doi:10.1145/3298689.3347058. hdl:11311/1108996. ISBN 978-1-4503-6243-6. S2CID 196831663. Retrieved October 16, 2019.
Rendle, Steffen; Krichene, Walid; Zhang, Li; Anderson, John (September 22, 2020). "Neural Collaborative Filtering vs. Matrix Factorization Revisited". Fourteenth ACM Conference on Recommender Systems. pp. 240–248. arXiv:2005.09683. doi:10.1145/3383313.3412488. ISBN 978-1-4503-7583-2.
Wu, L. (May 2023). "A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation". IEEE Transactions on Knowledge and Data Engineering. 35 (5): 4425–4445. arXiv:2104.13030. doi:10.1109/TKDE.2022.3145690.
Samek, W. (March 2021). "Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications". Proceedings of the IEEE. 109 (3): 247–278. arXiv:2003.07631. doi:10.1109/JPROC.2021.3060483.

belfercenter.org

Ovadya, Aviv (May 17, 2022). "Bridging-Based Ranking". Belfer Center at Harvard University. pp. 1, 14–28. Retrieved July 17, 2024.
Ovadya, Aviv (May 17, 2022). "Bridging-Based Ranking". Belfer Center for Science and International Affairs at Harvard University. pp. 21–23. Retrieved July 17, 2024.

boisestate.edu

scholarworks.boisestate.edu

Ekstrand, Michael (August 21, 2018). "The LKPY Package for Recommender Systems Experiments". Computer Science Faculty Publications and Presentations. Boise State University, ScholarWorks. doi:10.18122/cs_facpubs/147/boisestate.

books.google.com

Sanghack Lee and Jihoon Yang and Sung-Yong Park, Discovery of Hidden Similarity on Collaborative Filtering to Overcome Sparsity Problem, Discovery Science, 2007.

cloud.google.com

Google Cloud Blog. \"Scaling Deep Retrieval with Two-Tower Models.\" Published November 30, 2022. Accessed December 2024.

docear.org

Beel, J.; Genzmehr, M.; Gipp, B. (October 2013). "A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation" (PDF). Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation. pp. 7–14. doi:10.1145/2532508.2532511. ISBN 978-1-4503-2465-6. S2CID 8202591. Archived from the original (PDF) on April 17, 2016. Retrieved October 22, 2013.
Beel, J.; Langer, S.; Genzmehr, M.; Gipp, B.; Breitinger, C. (October 2013). "Research paper recommender system evaluation: A quantitative literature survey" (PDF). Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation. pp. 15–22. doi:10.1145/2532508.2532512. ISBN 978-1-4503-2465-6. S2CID 4411601.
Joeran Beel; Stefan Langer; Marcel Genzmehr; Andreas Nürnberger (September 2013). "Persistence in Recommender Systems: Giving the Same Recommendations to the Same Users Multiple Times" (PDF). In Trond Aalberg; Milena Dobreva; Christos Papatheodorou; Giannis Tsakonas; Charles Farrugia (eds.). Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013). Lecture Notes of Computer Science (LNCS). Vol. 8092. Springer. pp. 390–394. Retrieved November 1, 2013.
Joeran Beel; Stefan Langer; Andreas Nürnberger; Marcel Genzmehr (September 2013). "The Impact of Demographics (Age and Gender) and Other User Characteristics on Evaluating Recommender Systems" (PDF). In Trond Aalberg; Milena Dobreva; Christos Papatheodorou; Giannis Tsakonas; Charles Farrugia (eds.). Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013). Springer. pp. 400–404. Retrieved November 1, 2013.
Beel, Joeran, Langer, Stefan, Genzmehr, Marcel (September 2013). "Sponsored vs. Organic (Research Paper) Recommendations and the Impact of Labeling" (PDF). In Trond Aalberg, Milena Dobreva, Christos Papatheodorou, Giannis Tsakonas, Charles Farrugia (eds.). Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013). pp. 395–399. Retrieved December 2, 2013.

doi.org

Ricci, Francesco; Rokach, Lior; Shapira, Bracha (2022). "Recommender Systems: Techniques, Applications, and Challenges". In Ricci, Francesco; Rokach, Lior; Shapira, Bracha (eds.). Recommender Systems Handbook (3 ed.). New York: Springer. pp. 1–35. doi:10.1007/978-1-0716-2197-4_1. ISBN 978-1-0716-2196-7.
Roy, Deepjyoti; Dutta, Mala (2022). "A systematic review and research perspective on recommender systems". Journal of Big Data. 9 (59). doi:10.1186/s40537-022-00592-5.
Gupta, Pankaj; Goel, Ashish; Lin, Jimmy; Sharma, Aneesh; Wang, Dong; Zadeh, Reza (2013). "WTF: the who to follow service at Twitter". Proceedings of the 22nd International Conference on World Wide Web. Association for Computing Machinery. pp. 505–514. doi:10.1145/2488388.2488433. ISBN 9781450320351.
Baran, Remigiusz; Dziech, Andrzej; Zeja, Andrzej (June 1, 2018). "A capable multimedia content discovery platform based on visual content analysis and intelligent data enrichment". Multimedia Tools and Applications. 77 (11): 14077–14091. doi:10.1007/s11042-017-5014-1. ISSN 1573-7721. S2CID 36511631.
Chen, Hung-Hsuan; Gou, Liang; Zhang, Xiaolong; Giles, Clyde Lee (2011). "CollabSeer: a search engine for collaboration discovery" (PDF). Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries. Association for Computing Machinery. pp. 231–240. doi:10.1145/1998076.1998121. ISBN 9781450307444.
jobs (September 3, 2014). "How to tame the flood of literature : Nature News & Comment". Nature. 513 (7516). Nature.com: 129–130. doi:10.1038/513129a. PMID 25186906. S2CID 4460749.
Melville, Prem; Sindhwani, Vikas (2010). "Recommender Systems" (PDF). In Claude Sammut; Geoffrey I. Webb (eds.). Encyclopedia of Machine Learning. Springer. pp. 829–838. doi:10.1007/978-0-387-30164-8_705. ISBN 978-0-387-30164-8.
Haupt, Jon (June 1, 2009). "Last.fm: People-Powered Online Radio". Music Reference Services Quarterly. 12 (1–2): 23–24. doi:10.1080/10588160902816702. ISSN 1058-8167. S2CID 161141937.
Chen, Hung-Hsuan; Chen, Pu (January 9, 2019). "Differentiating Regularization Weights -- A Simple Mechanism to Alleviate Cold Start in Recommender Systems". ACM Transactions on Knowledge Discovery from Data. 13: 1–22. doi:10.1145/3285954. S2CID 59337456.
Rubens, Neil; Elahi, Mehdi; Sugiyama, Masashi; Kaplan, Dain (2016). "Active Learning in Recommender Systems". In Ricci, Francesco; Rokach, Lior; Shapira, Bracha (eds.). Recommender Systems Handbook (2 ed.). Springer US. pp. 809–846. doi:10.1007/978-1-4899-7637-6_24. ISBN 978-1-4899-7637-6.
Bobadilla, J.; Ortega, F.; Hernando, A.; Alcalá, J. (2011). "Improving collaborative filtering recommender system results and performance using genetic algorithms". Knowledge-Based Systems. 24 (8): 1310–1316. doi:10.1016/j.knosys.2011.06.005.
Elahi, Mehdi; Ricci, Francesco; Rubens, Neil (2016). "A survey of active learning in collaborative filtering recommender systems". Computer Science Review. 20: 29–50. doi:10.1016/j.cosrev.2016.05.002.
Bi, Xuan; Qu, Annie; Wang, Junhui; Shen, Xiaotong (2017). "A group-specific recommender system". Journal of the American Statistical Association. 112 (519): 1344–1353. doi:10.1080/01621459.2016.1219261. S2CID 125187672.
Ekstrand, Michael (August 21, 2018). "The LKPY Package for Recommender Systems Experiments". Computer Science Faculty Publications and Presentations. Boise State University, ScholarWorks. doi:10.18122/cs_facpubs/147/boisestate.
Vente, Tobias; Ekstrand, Michael; Beel, Joeran (September 14, 2023). "Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit". Proceedings of the 17th ACM Conference on Recommender Systems. ACM. pp. 1212–1216. doi:10.1145/3604915.3610656. ISBN 979-8-4007-0241-9.
Zhao, Wayne Xin; Mu, Shanlei; Hou, Yupeng; Lin, Zihan; Chen, Yushuo; Pan, Xingyu; Li, Kaiyuan; Lu, Yujie; Wang, Hui; Tian, Changxin; Min, Yingqian; Feng, Zhichao; Fan, Xinyan; Chen, Xu; Wang, Pengfei (October 26, 2021). "RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms". Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM. pp. 4653–4664. arXiv:2011.01731. doi:10.1145/3459637.3482016. ISBN 978-1-4503-8446-9.
Li, Jiayu; Li, Hanyu; He, Zhiyu; Ma, Weizhi; Sun, Peijie; Zhang, Min; Ma, Shaoping (October 8, 2024). "ReChorus2.0: A Modular and Task-Flexible Recommendation Library". 18th ACM Conference on Recommender Systems. ACM. pp. 454–464. doi:10.1145/3640457.3688076. ISBN 979-8-4007-0505-2.
Michiels, Lien; Verachtert, Robin; Goethals, Bart (September 18, 2022). "RecPack: An(other) Experimentation Toolkit for Top-N Recommendation using Implicit Feedback Data". Proceedings of the 16th ACM Conference on Recommender Systems. ACM. pp. 648–651. doi:10.1145/3523227.3551472. ISBN 978-1-4503-9278-5.
Montaner, M.; Lopez, B.; de la Rosa, J. L. (June 2003). "A Taxonomy of Recommender Agents on the Internet". Artificial Intelligence Review. 19 (4): 285–330. doi:10.1023/A:1022850703159. S2CID 16544257..
Adomavicius, G.; Tuzhilin, A. (June 2005). "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions". IEEE Transactions on Knowledge and Data Engineering. 17 (6): 734–749. CiteSeerX 10.1.1.107.2790. doi:10.1109/TKDE.2005.99. S2CID 206742345..
Herlocker, J. L.; Konstan, J. A.; Terveen, L. G.; Riedl, J. T. (January 2004). "Evaluating collaborative filtering recommender systems". ACM Trans. Inf. Syst. 22 (1): 5–53. CiteSeerX 10.1.1.78.8384. doi:10.1145/963770.963772. S2CID 207731647..
Beel, J.; Genzmehr, M.; Gipp, B. (October 2013). "A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation" (PDF). Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation. pp. 7–14. doi:10.1145/2532508.2532511. ISBN 978-1-4503-2465-6. S2CID 8202591. Archived from the original (PDF) on April 17, 2016. Retrieved October 22, 2013.
Beel, J.; Langer, S.; Genzmehr, M.; Gipp, B.; Breitinger, C. (October 2013). "Research paper recommender system evaluation: A quantitative literature survey" (PDF). Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation. pp. 15–22. doi:10.1145/2532508.2532512. ISBN 978-1-4503-2465-6. S2CID 4411601.
Beel, J.; Gipp, B.; Langer, S.; Breitinger, C. (July 26, 2015). "Research Paper Recommender Systems: A Literature Survey". International Journal on Digital Libraries. 17 (4): 305–338. doi:10.1007/s00799-015-0156-0. S2CID 207035184.
Koren, Yehuda; Volinsky, Chris (August 1, 2009). "Matrix Factorization Techniques for Recommender Systems". Computer. 42 (8): 30–37. CiteSeerX 10.1.1.147.8295. doi:10.1109/MC.2009.263. S2CID 58370896.
Felício, Crícia Z.; Paixão, Klérisson V.R.; Barcelos, Celia A.Z.; Preux, Philippe (July 9, 2017). "A Multi-Armed Bandit Model Selection for Cold-Start User Recommendation". Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (PDF). UMAP '17. Bratislava, Slovakia: Association for Computing Machinery. pp. 32–40. doi:10.1145/3079628.3079681. ISBN 978-1-4503-4635-1. S2CID 653908.
Wang, Donghui; Liang, Yanchun; Xu, Dong; Feng, Xiaoyue; Guan, Renchu (2018). "A content-based recommender system for computer science publications". Knowledge-Based Systems. 157: 1–9. doi:10.1016/j.knosys.2018.05.001.
Gomez-Uribe, Carlos A.; Hunt, Neil (December 28, 2015). "The Netflix Recommender System". ACM Transactions on Management Information Systems. 6 (4): 1–19. doi:10.1145/2843948.
Yifei, Ma; Narayanaswamy, Balakrishnan; Haibin, Lin; Hao, Ding (2020). "Temporal-Contextual Recommendation in Real-Time". Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Association for Computing Machinery. pp. 2291–2299. doi:10.1145/3394486.3403278. ISBN 978-1-4503-7998-4. S2CID 221191348.
Hidasi, Balázs; Karatzoglou, Alexandros (October 17, 2018). "Recurrent Neural Networks with Top-k Gains for Session-based Recommendations". Proceedings of the 27th ACM International Conference on Information and Knowledge Management. CIKM '18. Torino, Italy: Association for Computing Machinery. pp. 843–852. arXiv:1706.03847. doi:10.1145/3269206.3271761. ISBN 978-1-4503-6014-2. S2CID 1159769.
Li, Jing; Ren, Pengjie; Chen, Zhumin; Ren, Zhaochun; Lian, Tao; Ma, Jun (November 6, 2017). "Neural Attentive Session-based Recommendation". Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. CIKM '17. Singapore, Singapore: Association for Computing Machinery. pp. 1419–1428. arXiv:1711.04725. doi:10.1145/3132847.3132926. ISBN 978-1-4503-4918-5. S2CID 21066930.
Liu, Qiao; Zeng, Yifu; Mokhosi, Refuoe; Zhang, Haibin (July 19, 2018). "STAMP". Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD '18. London, United Kingdom: Association for Computing Machinery. pp. 1831–1839. doi:10.1145/3219819.3219950. ISBN 978-1-4503-5552-0. S2CID 50775765.
Zou, Lixin; Xia, Long; Ding, Zhuoye; Song, Jiaxing; Liu, Weidong; Yin, Dawei (2019). "Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems". Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD '19. pp. 2810–2818. arXiv:1902.05570. doi:10.1145/3292500.3330668. ISBN 978-1-4503-6201-6. S2CID 62903207.
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