A valuable small subset strategically selected from massive online reviews is beneficial to improve consumers’ decision-making efficiency in e-commerce. Existing review selection methods primarily concentrate on the informativeness of reviews and aim to find a subset of reviews that can reflect the informational properties of the original review set. However, changes in consumers’ review diets during the two-phase decision process are not fully considered. In this study, we propose a novel review selection problem of finding a diet-matched review subset with high diversity and representativeness, which can better adapt to consumers’ review-diet conversion from attribute-oriented to experience-oriented reviews between two decision phases. A novel decision-phase-based review selection method named DPRS is further proposed, which involves two steps: review classification and review selection. In the review classification step, the probability of a review being attribute-oriented or experience-oriented is estimated by prior knowledge-aware attentive neural network. In the second step, a novel heuristic algorithm, namely, stepwise non-dominated selection with superiority strategy, is introduced to seek the solution to the review selection problem. Extensive experiments on a real-world dataset demonstrate that DPRS outperforms state-of-the-art methods in terms of both review classification and review selection.

It is known that the slope probability density function (pdf) of the sea surface is non-Gaussian and can be expressed as a Gram-Charlier to fouth-order expension, including seven parameters: slope variances in upwind and crosswind direction, two skewness coefficients and three peakedness coefficients. Cox and Munk[1] had gotten all the seven parameters varying with the wind speed using optical data. However, those parameters for the sea surface at optical band and microwave band are different, and until now it is not clear for microwave band what values the seven parameters are. Wave scatterometer is a kind of microwave sensor specially designed for wave spectrum remote sensing and could provide σ0in high resolution of the range and azimuthal direction so that it is possible to remote sense the slope pdf for a certain space-time. In this paper, firstly, the accuracy of Geometric Optical (GO) model is evaluated, which is used for confirming the valid range of incidence angle to invert; then, a two dimensional (2D) inversion method is developed for remote sensing all the seven parameters in non-Gaussian slope pdf by the wave scatterometer based on GO model; finally those parameters are inverted by the method using the Ku-band airborne wave scatterometer KuROS measurements.