A two-stage multiple-factor aware method for travel product recommendation

Abstract

The great quantity of travel products available online has increased demand for travel product recommendation system. Due to the relatively high value and time cost of travel products, users consider more factors (personal preference, social preference and seasonality factor etc.) in making this type of low-frequent purchase decisions, compared to other products (e.g. music, movies or news). Thus, recommending travel products generally faces sparsity and complexity problems. In this study, we propose a two-stage multiple-factor aware method named TSMFA. In the topic stage, a user-topic matrix is constructed using travel products’ topic attributions to alleviate sparsity problem, while a preference-aware topic selection is introduced to consider both social and personal preference in recommendation. In the product stage, seasonal prevalence is employed to adjust the recommended product order to incorporate seasonality factor. The proposed method is validated with real transaction dataset from a leading OTA (Online Travel Agent) website in western China. The experimental results demonstrate that it outperforms the state-of-the-art recommendation methods in terms of effectiveness and usefulness.