{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T18:24:17Z","timestamp":1771525457443,"version":"3.50.1"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2019,2,13]],"date-time":"2019-02-13T00:00:00Z","timestamp":1550016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"SURF Cooperative"},{"name":"Dutch National e-Infrastructure"},{"name":"EU FP7 project CrowdRec","award":["610594"],"award-info":[{"award-number":["610594"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2019,4,30]]},"abstract":"<jats:p>\n            User interactions can be considered to constitute different feedback channels, for example, view, click, like or follow, that provide implicit information on users\u2019 preferences. Each implicit feedback channel typically carries a unary, positive-only signal that can be exploited by collaborative filtering models to generate lists of personalized recommendations. This article investigates how a learning-to-rank recommender system can best take advantage of implicit feedback signals from multiple channels. We focus on Factorization Machines (FMs) with Bayesian Personalized Ranking (BPR), a pairwise learning-to-rank method, that allows us to experiment with different forms of exploitation. We perform extensive experiments on three datasets with multiple types of feedback to arrive at a series of insights. We compare conventional, direct integration of feedback types with our proposed method, which exploits multiple feedback channels during the\n            <jats:italic>sampling<\/jats:italic>\n            process of training. We refer to our method as\n            <jats:italic>multi-channel<\/jats:italic>\n            sampling. Our results show that multi-channel sampling outperforms conventional integration, and that sampling with the relative \u201clevel\u201d of feedback is always superior to a level-blind sampling approach. We evaluate our method experimentally on three datasets in different domains and observe that with our multi-channel sampler the accuracy of recommendations can be improved considerably compared to the state-of-the-art models. Further experiments reveal that the appropriate sampling method depends on particular properties of datasets such as popularity skewness.\n          <\/jats:p>","DOI":"10.1145\/3291756","type":"journal-article","created":{"date-parts":[[2019,2,14]],"date-time":"2019-02-14T19:36:17Z","timestamp":1550172977000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Top-N Recommendation with Multi-Channel Positive Feedback using Factorization Machines"],"prefix":"10.1145","volume":"37","author":[{"given":"Babak","family":"Loni","sequence":"first","affiliation":[{"name":"Delft University of Technology, The Netherlands"}]},{"given":"Roberto","family":"Pagano","sequence":"additional","affiliation":[{"name":"Politecnico di Milano and Delft University of Technology, The Netherlands"}]},{"given":"Martha","family":"Larson","sequence":"additional","affiliation":[{"name":"Delft University of Technology and Radboud University, The Netherlands"}]},{"given":"Alan","family":"Hanjalic","sequence":"additional","affiliation":[{"name":"Delft University of Technology, The Netherlands"}]}],"member":"320","published-online":{"date-parts":[[2019,2,13]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2015.09.007"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 2011 International Conference on KDD Cup (KDDCUP'11)","volume":"18","author":"Gantner Zeno","year":"2011","unstructured":"Zeno Gantner , Lucas Drumond , Christoph Freudenthaler , and Lars Schmidt-Thieme . 2011 . Personalized Ranking for Non-uniformly Sampled Items . In Proceedings of the 2011 International Conference on KDD Cup (KDDCUP'11) , Vol. 18 . JMLR.org, 231--247. Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2011. Personalized Ranking for Non-uniformly Sampled Items. In Proceedings of the 2011 International Conference on KDD Cup (KDDCUP'11), Vol. 18. JMLR.org, 231--247."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.05.074"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.22"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511763113"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959134"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864727"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2507157.2508063"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401944"},{"key":"e_1_2_1_10_1","volume-title":"A comparative study of collaborative filtering algorithms. Computing Research Repository abs\/1205.3193","author":"Lee Joonseok","year":"2012","unstructured":"Joonseok Lee , Mingxuan Sun , and Guy Lebanon . 2012. A comparative study of collaborative filtering algorithms. Computing Research Repository abs\/1205.3193 ( 2012 ). arxiv:1205.3193. Joonseok Lee, Mingxuan Sun, and Guy Lebanon. 2012. A comparative study of collaborative filtering algorithms. Computing Research Repository abs\/1205.3193 (2012). arxiv:1205.3193."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2645710.2645759"},{"key":"e_1_2_1_12_1","volume-title":"Exploiting explicit and implicit feedback for personalized ranking. Mathematical Problems in Engineering 2016 (01","author":"Li Gai","year":"2016","unstructured":"Gai Li and Qiang Chen . 2016. Exploiting explicit and implicit feedback for personalized ranking. Mathematical Problems in Engineering 2016 (01 2016 ), 1--11. Gai Li and Qiang Chen. 2016. Exploiting explicit and implicit feedback for personalized ranking. Mathematical Problems in Engineering 2016 (01 2016), 1--11."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-41230-1_11"},{"key":"e_1_2_1_14_1","volume-title":"Factorization Machines for Datasets with Implicit Feedback. Computing Research Repository abs\/1812.08254","author":"Loni Babak","year":"2018","unstructured":"Babak Loni , Martha Larson , and Alan Hanjalic . 2018. Factorization Machines for Datasets with Implicit Feedback. Computing Research Repository abs\/1812.08254 ( 2018 ). arXiv:1812.08254. Babak Loni, Martha Larson, and Alan Hanjalic. 2018. Factorization Machines for Datasets with Implicit Feedback. Computing Research Repository abs\/1812.08254 (2018). arXiv:1812.08254."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959163"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-06028-6_72"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2600428.2609623"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2014.09.013"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2505648"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.127"},{"key":"e_1_2_1_21_1","volume-title":"Context-aware Ranking with Factorization Models","author":"Rendle Steffen","unstructured":"Steffen Rendle . 2011. Context-aware Ranking with Factorization Models . Springer . Steffen Rendle. 2011. Context-aware Ranking with Factorization Models. Springer."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2168752.2168771"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2556195.2556248"},{"key":"e_1_2_1_24_1","volume-title":"Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI'09)","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle , Christoph Freudenthaler , Zeno Gantner , and Lars Schmidt-Thieme . 2009 . BPR: Bayesian personalized ranking from implicit feedback . In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI'09) . AUAI Press, 452--461. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI'09). AUAI Press, 452--461."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2009916.2010002"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2556270"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835895"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939690"},{"key":"e_1_2_1_29_1","volume-title":"Representation learning and pairwise ranking for implicit and explicit feedback in recommendation systems. Computing Research Repository abs\/1705.00105","author":"Trofimov Mikhail","year":"2017","unstructured":"Mikhail Trofimov , Sumit Sidana , Oleh Horodnitskii , Charlotte Laclau , Yury Maximov , and Massih-Reza Amini . 2017. Representation learning and pairwise ranking for implicit and explicit feedback in recommendation systems. 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