In this paper, we study the personalized ranking problem for item’s recommendation text (product description). Item’s recommendation text is the sentence that describe the item highlights for user decision (such as buy or click). The recommendation texts is shown under the item, and we also call them rec-texts. One item has multiple rec-texts, and different rec-texts have different affect for user decision. So the problem is to capture the user preference for each item’s rec-texts and to personalized display. In this paper, we study multiple methods to train a model which learn the user preference scores for each item’s rec-texts. The online experimental results demonstrate our methods work.
