Adaptive Diversity in Recommender Systems
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چکیده
The evaluation of a recommendation engine cannot rely only on the accuracy of provided recommendations. One should consider additional dimensions, such as diversity of provided suggestions, in order to guarantee heterogeneity in the recommendation list. In this paper we analyse users’ propensity in selecting diverse items, by taking into account content-based item attributes. Individual propensity to diversification is used to re-rank the list of Top-N items predicted by a recommendation algorithm, with the aim of fostering diversity in the final ranking. We show experimental results that confirm the validity of our modelling approach.
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تاریخ انتشار 2015