| dc.description.abstract | In very competitive and fast changing online marketplaces, many companies have been providing various amounts of online services and applications to gain sustainable competitive advantages. However, to develop high quality services and applications which meet users requirements and preferences, providers always need to know and predict their users transaction behavior. These behaviors vary from systems since users interact with different systems e.g. online shopping, online auction etc., that hosted by different providers.
The prediction approaches can be developed based on whether contextual information/explict ratings or transactional data/implicit feedbacks. However, in practice, since contextual information and explicit feedbacks are obtained by asking users to provide and update information about their identity and interests, this information is difficult to collect and not always available. In some cases, for example, in real time closing price prediction model for business–to-business (B2B) reverse online auction, even the contextual information less contributes for prediction. Therefore, this study aims to develop efficient prediction approaches based on user’s transactional data/implicit feedback which are purchasing and bidding data and proposes two prediction approaches.
The first prediction approach strives to predict closing price and duration of B2B reverse online auction, while second approach predicts user’s future preference for recommending items in m-commerce. The simulation of the first approach is based on real time auction data derived from a B2B online auction marketplace over a two-year period. Simulation results show that after observing the first 4 bids, this methodology can predict closing prices and duration with 84.6 % and 71.9 % accuracy, respectively. To assess the efficiency and stability of the second prediction approach, the several experiments are conducted on purchasing data collected from a movie website hosting by a 3G service provider. The experimental results show that the second approach improves the accuracy of traditional content-based approach by 1.15 times on average.
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