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Applying Optimal Weight Combination in Hybrid Recommender Systems

Haubner, Nicolas; Setzer, Thomas


We propose a method for learning weighting schemes in weighted hybrid recommender systems (RS) that is based on statistical forecast and portfolio theory. An RS predicts the future preference of a set of items for a user, and recommends the top items. A hybrid RS combines individual RS in making the predictions. To determine the weighting of individual RS, we learn so-called optimal weights from the covariance matrix of available error data of individual RS that minimize the error of a combined RS. We test the method on the well-known MovieLens 1M dataset, and, contrary to the “forecast combination puzzle”, stating that a simple average (SA) weighting typically outperforms learned weights, the out-of-sample results show that the learned weights consistently outperform the individually best RS as well as an SA combination.

Verlagsausgabe §
DOI: 10.5445/IR/1000135223
Veröffentlicht am 12.07.2021
DOI: 10.24251/HICSS.2020.191
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2020
Sprache Englisch
Identifikator ISBN: 978-0-9981331-3-3
KITopen-ID: 1000135223
Erschienen in Proceedings of the 53rd Hawaii International Conference on System Sciences
Veranstaltung 53rd Hawaii International Conference on System Sciences (HICSS 2020), Grand Wailea, Maui, Hawaii, 07.01.2020 – 10.01.2020
Seiten 1552-1561
Vorab online veröffentlicht am 07.01.2020
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