Recommender systems in academia and industry typically predict Click-Through Rate (CTR) at the item or entity level. In practical scenarios, products can take on various forms and designs. We present a novel joint scoring framework that supports the listwise ranking of a collection composed of multiple entities. It learns the best combination of entities to be displayed to the user. We also introduce a novel dual attention mechanism that better captures the user's interest in the collection. Our approach demonstrated superior performance through offline and online experiments and has been deployed to Shopee’s Shop Ads across all markets.