Ensemble Recommendations via Thompson Sampling: an Experimental Study within e-Commerce

Ensemble Recommendations via Thompson Sampling : an Experimental Study within e-Commerce

Nilsson, Bengt J. (author): Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT)

Paraschakis, Dimitris, 1980- (author): Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT)

(creator_code:org_t)

2018-03-05
2018
English.
In: Proceeding IUI '18 23rd International Conference on Intelligent User Interfaces. - New York, NY, USA : ACM Digital Library. ; , s. 19-29

Related links:: https://iui.acm.org/...; show more...; https://mau.diva-por... (primary) (Raw object); https://urn.kb.se/re...; https://doi.org/10.1...; show less...

Abstract Subject headings

This work presents an extension of Thompson Sampling bandit policy for orchestrating the collection of base recommendation algorithms for e-commerce. We focus on the problem of item-to-item recommendations, for which multiple behavioral and attribute-based predictors are provided to an ensemble learner. We show how to adapt Thompson Sampling to realistic situations when neither action availability nor reward stationarity is guaranteed. Furthermore, we investigate the effects of priming the sampler with pre-set parameters of reward probability distributions by utilizing the product catalog and/or event history, when such information is available. We report our experimental results based on the analysis of three real-world e-commerce datasets.

By the author/editor: Brodén, Björn; Hammar, Mikael; Nilsson, Bengt J ...; Paraschakis, Dim ...

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.