Improving Text-Based Similar Product Recommendation for Dynamic Product Advertising at Yahoo
Retrieving similar products is a critical functionality required by many e-commerce websites as well as dynamic product advertising systems. Retargeting and Prospecting are two major forms of dynamic product advertising. Typically, after a user interacts with a product on an advertiser website (e.g., Macy’s), when the user later visits a website (e.g., yahoo.com) supported by a dynamic product advertising system, the same product may be shown to the user as a Retargeting product ad, while some similar products may be shown to the user as Prospecting product ads on the web page. Similar products can enrich users’ ad experience based on users’ intent on the Prospecting product ads through which the users interacted. These product ads can also serve as substitutes when Retargeting ad candidates are out of stock. However, it is challenging to retrieve similar products among billions of products in a product catalog efficiently. Deep Siamese models allow efficient retrieval but do not put enough emphasize on key product attributes. To improve the quality of the similar products, we propose to first use a Siamese Transformer-based model to retrieve similar products and then refine them with the attribute “product name” that indicates the type of a product (e.g., running shoes, engagement ring, etc.) for post filtering. We propose a novel product name generation model that fine tunes a pre-trained Transformer-based language model with a sequence to sequence objective. To the best of our knowledge, this is the first work using a generative approach for identifying product attributes. We introduce two applications of the proposed approach for the dynamic product advertising system of Yahoo for Retargeting and Prospecting respectively. Offline evaluation and online A/B testing shows that the proposed approach retrieves high quality similar products, leading to an increase of ad clicks and ad revenue.
