| Resumen |
Collaborative ?ltering is one of the most common approaches
in many current recommender systems. However, historical
data and customer pro?les, 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-bene?t 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. |
