Active Evaluation and Ranking of Multiple-Attribute Items Using Feedforward Neural Networks

In this paper, an interactive method for ranking multiple-attribute items from user's feedback using a feedforward neural network (NN) has been presented. The task of ranking multiple-attribute items is relevant in different tasks of e-commerce including request for quotes (RFQ), negotiations, personalized catalogs, profiling, and customer modeling. Often, a linear parametric function is used to model the overall value function considering known individual attribute value functions, and the parameters of the linear function are estimated by linear programming (LP). In this paper, we propose an NN-based active learning method for evaluating and ranking the multiple-attribute items (or bids in RFQ) without imposing the restriction of linear dependence between the attribute value functions. Use of an NN also relaxes the constraint of known parametric form of individual attribute value functions, which is usually assumed in LP-based methods. A suitable objective error measure is defined in this context, and correspondingly, a feedforward NN is trained to obtain the ranking of the item set (or bids). Effectiveness of the method is validated on real-life data sets.