Interest modeling in recommender system has been a constant topic for improving user experience, and typical interest modeling tasks (e.g. multi-interest, long-tail interest and long-term interest) have been investigated in many existing works. However, most of them only consider one interest in isolation, while neglecting their interrelationships. In this paper, we argue that these tasks suffer from a common "interest amnesia" problem, and a solution exists to mitigate it simultaneously. We propose a novel and unified framework in the retrieval stage, "Trinity", to solve interest amnesia problem and improve multiple interest modeling tasks. We construct a real-time clustering system that enables us to project items into enumerable clusters, and calculate statistical interest histograms over these clusters. Based on these histograms, Trinity recognizes underdelivered themes and remains stable when facing emerging hot topics. Its derived retrievers have been deployed on the recommender system of Douyin, significantly improving user experience and retention. We believe that such practical experience can be well generalized to other scenarios.

Selecting elements for graphic design is essential for ensuring a correct understanding of clients' requirements as well as improving the efficiency of designers before a fine-designed process. Some semi-automatic design tools proposed layout templates where designers always select elements according to the rectangular boxes that specify how elements are placed. In practice, layout and element selection are complementary. Compared to the layout which can be readily obtained from pre-designed templates, it is generally time-consuming to mindfully pick out suitable elements, which calls for an automation of elements selection. To address this, we formulate element selection as a sequential decision-making process and develop a deep element selection network (DESN). Given a layout file with annotated elements, new graphical elements are selected to form graphic designs based on aesthetics and consistency criteria. To train our DESN, we propose an end-to-end, reinforcement learning based framework, where we design a novel reward function that jointly accounts for visual aesthetics and consistency. Based on this, visually readable and aesthetic drafts can be efficiently generated. We further contribute a layout-poster dataset with exhaustively labeled attributes of poster key elements. Qualitative and quantitative results indicate the efficacy of our approach.