A price prediction model for online auctions using fuzzy reasoning techniques

E-consumers are urged to opt for the best bidding strategies to excel in the competitive environment of multiple and simultaneous online auctions for same or similar items. It becomes very complicated for the bidders to make the decisions of selecting which auction to participate in, place single or multiple bids, early or late bidding and how much to bid. In this paper, we present the design of an autonomous dynamic bidding agent (ADBA) that makes these decisions on behalf of the buyers according to their bidding behaviors. The agent develops a comprehensive methodology for initial price estimation and an integrated model for final price prediction. The initial price estimation methodology selects an auction to participate in and assesses the value (initial price) of the auctioned item. Then the final price prediction model forecasts the bid amount by designing different bidding strategies using fuzzy reasoning techniques. The experimental results demonstrated improved initial price prediction outcomes by proposing a clustering based approach. Also, the results show the proficiency of the fuzzy bidding strategies in terms of their success rate and expected utility.