EXPERT ADVICE AND REGRET FOR SERIAL RECOMMENDERS

Anton Eliëns, Yiwen Wang

abstract

In this paper we propose a tentative framework (R3) for adapting a sequence of predictions (guided tour) generated by what we call a serial recommender. The R3 framework (rate, recommend, regret) is applied to the construction of personalized guided tours, based on expert advice, in the domain of cultural heritage, in particular digital dossiers about contemporary art. Guided tours are in first instance obtained by tracking expert users. Our proposal is based on a variant of decision theory, that uses a regret function to measure the difference between a proposed decision and a finite collection of expert decisions. In our framework, personalization may then be seen as a minimization problem over a weighting scheme, expressing the relative importance of experts of which tours are available. Our aim in this paper is to arrive at a formalization of the recommendation of sequences (guided tours) that allows for adaptation to individual user preferences by a revision of the weight attached to a particular advice based on user feedback.
keywords: decision theory, personalization, guided tours, digital dossier, cultural heritage

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