Resumen
Collaborative filtering is one of the most common approaches in many current recommender systems. However, historical data and customer profiles, necessary for this approach, are not always available. Similarly, new products are constantly launched to the market lacking historical information. We propose a new method to deal with these "cold start" scenarios, designing price-estimation functions used for making recommendations based on cost-benefit analysis. Experimental results, using a data set of 836 laptop descriptions, showed that such price-estimation functions can be learned from data. Besides, they can also be used to formulate interpretable recommendations that explain to users how product features determine its price. Finally a 2D visualization of the proposed recommender system was provided.
Idioma original | Inglés |
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Páginas (desde-hasta) | 27-34 |
Número de páginas | 8 |
Publicación | CEUR Workshop Proceedings |
Volumen | 811 |
Estado | Publicada - 2011 |
Evento | Joint Workshop on Human Decision Making in Recommender Systems, Decisions@RecSys 2011 and User-Centric Evaluation of Recommender Systems and Their Interfaces-2, UCERSTI 2 - Affiliated with the 5th ACM Conference on Recommender Systems, RecSys 2011 - Chicago, IL, Estados Unidos Duración: 23 oct. 2011 → 26 oct. 2011 |