Bayesian Personalized Ranking with Multi-Channel User Feedback

Babak Loni, Roberto Pagano, Martha Larson, Alan Hanjalic

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

119 Citations (Scopus)


Pairwise learning-to-rank algorithms have been shown to allow recommendersystems to leverage unary user feedback. We proposeMulti-feedback Bayesian Personalized Ranking (MF-BPR), a pairwisemethod that exploits different types of feedback with an extendedsampling method. The feedback types are drawn from different“channels”, in which users interact with items (e.g., clicks,likes, listens, follows, and purchases). We build on the insight thatdifferent kinds of feedback, e.g., a click versus a like, reflect differentlevels of commitment or preference. Our approach differs fromprevious work in that it exploits multiple sources of feedback simultaneouslyduring the training process. The novelty of MF-BPRis an extended sampling method that equates feedback sources with“levels” that reflect the expected contribution of the signal. Wedemonstrate the effectiveness of our approach with a series of experimentscarried out on three datasets containing multiple typesof feedback. Our experimental results demonstrate that with a rightsampling method, MF-BPR outperforms BPR in terms of accuracy.We find that the advantage of MF-BPR lies in its ability to leveragelevel information when sampling negative items.
Original languageEnglish
Title of host publicationRecSys'16 Proceedings of the 10th ACM Conference on Recommender Systems
EditorsS. Sen, W. Geyer
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages4
ISBN (Electronic)978-1-4503-4035-9
Publication statusPublished - 2016
Event10th ACM Conference on Recommender Systems, RecSys 2016 - MIT, Boston, MA, United States
Duration: 15 Sept 201619 Sept 2016


Conference10th ACM Conference on Recommender Systems, RecSys 2016
Country/TerritoryUnited States
CityBoston, MA
Internet address


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