Product posters blend striking visuals with informative text to highlight the product and capture customer attention. However, crafting appealing posters and manually optimizing them based on online performance is laborious and resource-consuming. To address this, we introduce AutoPP, an automated pipeline for product poster generation and optimization that eliminates the need for human intervention. Specifically, the generator, relying solely on basic product information, first uses a unified design module to integrate the three key elements of a poster (background, text, and layout) into a cohesive output. Then, an element rendering module encodes these elements into condition tokens, efficiently and controllably generating the product poster. Based on the generated poster, the optimizer enhances its Click-Through Rate (CTR) by leveraging online feedback. It systematically replaces elements to gather fine-grained CTR comparisons and utilizes Isolated Direct Preference Optimization (IDPO) to attribute CTR gains to isolated elements. Our work is supported by AutoPP1M, the largest dataset specifically designed for product poster generation and optimization, which contains one million high-quality posters and feedback collected from over one million users. Experiments demonstrate that AutoPP achieves state-of-the-art results in both offline and online settings.

Path inference aims to reveal missing paths given a few number of GPS samples associated with a moving object by exploiting the topology of road network and statistical information of historical GPS trajectories, and plays a vital role in data preprocessing of location based information services. But, in practice path inference severely suffers from the data sparsity as well as the randomness of drivers path selection behaviors. In this paper, we propose a novel Bayesian path inference model subject to spatiotemporal constraints by taking into account the drivers path selection behaviors. To be specific, the problem of path inference is cast as the problem of searching K most probable candidate paths according to the joint posterior selection probabilities of candidate paths. When estimating model parameters, we use the frequency of each road segment in the historical GPS trajectories instead of that of road segment transfers to mitigate the influence of data sparsity. In addition, both spatiotemporal constraints and probability thresholds are introduced to narrow the search space, which significantly improves the time efficiency. The experiments are conducted using practical data and show that the proposed model is significantly superior to three existing popular models. When the GPS sampling interval varies from 1 minute to 5 minutes, the accuracy of the proposed method is 0.94, 0.91, 0.86, 0.80 and 0.74, and the Jaccard similarity 0.89, 0.85, 0.83, 0.80 and 0.75 respectively, the average improvement in accuracy rises from 3.68% to 18.69% and that in the Jaccard similarity from 4.56% to 18.42%.