Applying Optimal Weight Combination in Hybrid Recommender Systems

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dc.contributor.authorHaubner, Nicolas
dc.contributor.authorSetzer, Thomas
dc.date.accessioned2020-01-04T07:28:06Z
dc.date.available2020-01-04T07:28:06Z
dc.date.issued2020-01-07
dc.description.abstractWe 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.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.191
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/63930
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectService Analytics
dc.subjectforecast combination puzzle
dc.subjecthybrid recommender systems
dc.subjectoptimal weights
dc.titleApplying Optimal Weight Combination in Hybrid Recommender Systems
dc.typeConference Paper
dc.type.dcmiText

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