Human Curation and Convnets: Powering Item-to-Item Recommendations on Pinterest
By Dmitry Kislyuk, David C. Liu, Eric Tzeng, Yushi Jing. 2015
13 citations recorded by the source corpus.
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.
Subfields: Recommender Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Kaiming He
- Ross B. Girshick
- Abhinav Kumar Gupta
- Trevor Darrell
- Xinlei Chen
- Wanli Ouyang
- Dacheng Tao
- Yi Yang
Citations in the corpus
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015, methodology): “Our fast and effective object detection system has also been built in commercial systems such as at Pinterests [17], with user engagement improvements reported.”
- Related Pins at Pinterest: The Evolution of a Real-World Recommender System (2017, background): “…from pin descriptions, links, board titles, and other metadata Image features : fc6 and fc8 activations of a deep convolutional neural network [15] Word embeddings : aggregated for the text annotations [20] Category vector : aggregated from user-selected categories for their boards Topic…”