{"type":"ScholarlyArticle","title":"Image Feature Learning for Cold Start Problem in Display Advertising","authors":[{"name":"Kaixiang Mo","slug":"kaixiang-mo","given":"Kaixiang","family":"Mo","affiliation":""},{"name":"Bo Liu","slug":"bo-liu-2","given":"Bo","family":"Liu","affiliation":""},{"name":"Lei Xiao","slug":"lei-xiao","given":"Lei","family":"Xiao","affiliation":""},{"name":"Yong Li","slug":"yong-li-2","given":"Yong","family":"Li","affiliation":""},{"name":"Jie Jiang","slug":"jie-jiang","given":"Jie","family":"Jiang","affiliation":""}],"abstract":"In online display advertising, state-of-the-art Click Through Rate(CTR) prediction algorithms rely heavily on historical information, and they work poorly on growing number of new ads without any historical information. This is known as the the cold start problem. For image ads, current state-of-the-art systems use handcrafted image features such as multimedia features and SIFT features to capture the attractiveness of ads. However, these handcrafted features are task dependent, inflexible and heuristic. In order to tackle the cold start problem in image display ads, we propose a new feature learning architecture to learn the most discriminative image features directly from raw pixels and user feedback in the target task. The proposed method is flexible and does not depend on human heuristic. Extensive experiments on a real world dataset with 47 billion records show that our feature learning method outperforms existing handcrafted features significantly, and it can extract discriminative and meaningful features.","doi":null,"url":"https://ijcai.org/proceedings/2015/524","pdf":"https://ijcai.org/proceedings/2015/0524.pdf","markdown":"https://ijcai.org/proceedings/2015/524.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/524","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Machine Learning","track":"Special Track on Machine Learning","number":524,"pages":{"first":3728,"last":3733},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}