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Applying Optimal Weight Combination in Hybrid Recommender Systems

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Title:Applying Optimal Weight Combination in Hybrid Recommender Systems
Authors:Haubner, Nicolas
Setzer, Thomas
Keywords:Service Analytics
forecast combination puzzle
hybrid recommender systems
optimal weights
Date Issued:07 Jan 2020
Abstract: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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63930
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.191
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Service Analytics


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