given item, multiple selling points and descriptions are needed for personalized recommendations. Fig. \[fig:one-to-many-example\] shows an example. With plenty of recommendation texts, different sentences can be selected to display to different users to meet their preferences or to the same user at various situations so that he/she does not feel monotonous.

We summarize the above applications as the “one source, multiple targets” problems. As conventional encoder-decoder models encode same input patterns to same unique representative vectors without any variation, their ability of generating different sentences from one input are limited. Researchers have made efforts to improve the encoder-decoder models for more diverse generations. In the early periods, methods are proposed to interfere the inference stage of a well-trained encoder-decoder model to encourage abundant outputs [@li2016diversity; @vijayakumar2016diverse]. The drawback of such methods is that they do not optimize the encoder-decoder models to fit multi-target data and the quality of their generating results is limited by the trade-off between accuracy and diversity. Recently, variational encoder-decoders have shown great potentials in solving the “one source, multiple targets" problems [@bowman2016generating; @zhao2017learning; @shen2018improving]. These methods introduced an intermediate latent variable and assume that each configuration of the latent variable corresponds to a feasible response. Thus 