Human Curation and Convnets: Powering Item-to-Item Recommendations on Pinterest
This work addresses recommendation accuracy for Pinterest users, though it appears incremental by integrating existing methods.
The paper tackled item-to-item recommendation on Pinterest by combining collaborative filtering with content-based ranking, demonstrating that user curation signals and visual features from convnets improved user engagement rates.
This paper presents Pinterest Related Pins, an item-to-item recommendation system that combines collaborative filtering with content-based ranking. We demonstrate that signals derived from user curation, the activity of users organizing content, are highly effective when used in conjunction with content-based ranking. This paper also demonstrates the effectiveness of visual features, such as image or object representations learned from convnets, in improving the user engagement rate of our item-to-item recommendation system.