Large-Scale Price Interval Prediction at OTA Sites

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With the rapid growing proliferation of online travel agent (OTA) services, personalized recommendations are highly valuable as they can improve customer experiences by preventing the information overload problem. The accurate prediction of users’ expectations for price plays an important role in the personalized recommendation of hotels in OTA platforms. Considering that customers’ preferences for hotel prices are actually acceptable ranges and traditional point estimations may neglect some informative aspects of the prediction, interval estimation is more suitable for the problem investigated in this paper. However, existing related methods are not applicable due to some specific issues. To provide a better personalized recommendation of hotels in OTA platforms, this paper proposes a novel interval forecasting solution to improve the accuracy of predicting users’ price preferences. The novel interval forecasting solution first puts forward a customized objective function which could directly measure the quality of constructed intervals, while also allowing for adjustable tradeoffs between interval tightness and prediction reliability. Then, it combines alternating direction optimization and the gradient boosting framework to efficiently aggregate weak individual predictors to optimize the introduced learning objective. Empirical comparisons conducted on several benchmark standard datasets and a large-scale dataset shared with us by a major Chinese OTA site demonstrate the effectiveness of the proposed approach.