Propagating Maximum Capacities for Recommendation

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

Neighborhood-based approaches often fail in sparse scenarios; a direct implication for recommender systems exploiting co-occurring items is often an inappropriately poor performance. As a remedy, we propose to propagate information (e.g., similarities) across the item graph to leverage sparse data. Instead of processing only directly connected items (e.g. co-occurrences), the similarity of two items is defined as the maximum capacity path interconnecting them. Our approach resembles a generalization of neighborhood-based methods that are obtained as special cases when restricting path lengths to one. We present two efficient online computation schemes and report on empirical results.

Original languageEnglish
Title of host publicationKI 2017: Advances in Artificial Intelligence : 40th Annual German Conference on AI, Dortmund, Germany, September 25-29, 2017 : proceedings
Number of pages13
Place of PublicationCham, Schweiz
PublisherSpringer
Publication date2017
Pages72-84
ISBN (print)978-3-319-67189-5
ISBN (electronic)978-3-319-67190-1
DOIs
Publication statusPublished - 2017
Event40th German Conference on Artificial Intelligence - KI 2017 - Technische Universität Dortmund, Dortmund, Germany
Duration: 25.09.201729.09.2017
Conference number: 40
http://ki2017.tu-dortmund.de

    Research areas

  • Business informatics - Recommender systems, Maximum capacity paths, Information propagation, Sparsity, Co-occurrence, Cold-start problem