Tri-Training based Bilateral Multi-Issue Negotiation Framework

Negotiation has been extensively discussed in electronic commerce for decades. Recent growing interest in importing machine learning algorithm in electronic commerce has given increased importance to automated negotiation. A Tri-Training based algorithm was proposed to learn opponent’s negotiation preference. The process of negotiation was viewed as a proposal’s sequence which can be mapped into bidding trajectory feature space to form sample set. Due to fierce competition, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, Tri-Training, as a semisupervised method, was imported into negotiation framework to increase the number of samples and improve perdition accuracy of opponent’s negotiation preference learning. Based on negotiation preference of both side, an optimization algorithm is conducted to compute win-win counter proposal. The experimental results show that the proposed method can decrease the number of negotiation steps and increase the overall utility of negotiation.