Travis Ebesu - I am a Machine Learning Engineer at Pinterest working on related pins (closeup) recommendations. Previously, I worked on e-commerce product search at Walmart Labs. I completed my Ph.D. in recommender systems and deep learning at Santa Clara University advised by Dr. Yi Fang. My research interests include deep learning, recommender systems, search and natural language processing.

Education

• Ph.D. in Computer Science & Engineering, Santa Clara University, 2019
• M.S. in Computer Science & Engineering, Santa Clara University, 2015
• B.S. in Computer Science, Chaminade University

Publications

• Archana Godavarthy, Yuan Wang, Travis Ebesu, Suthee Chaidaroon, Yi Fang. Learning User Preferences through Online Conversations via Personalized Memory Transfer. In Information Retrieval Journal (IRJ), Springer, 2022. [PDF]
• Travis Ebesu. Deep Learning for Recommender Systems. Ph.D. thesis Santa Clara University, 2019. [PDF]
• Travis Ebesu, Bin Shen, Yi Fang. Collaborative Memory Network for Recommendation Systems. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, 2018. [PDF] [Slides] [Code]
• Suthee Chaidaroon, Travis Ebesu, Yi Fang. Deep Semantic Text Hashing with Weak Supervision. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, 2018. [PDF] [Code]
• Yogesh Jhamb, Travis Ebesu, Yi Fang. Attentive Contextual Denoising Autoencoder For Recommendation. In Proceedings of the ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR), 2018. [PDF] [Code]
• Travis Ebesu, Yi Fang. Neural Citation Network for Context-Aware Citation Recommendation. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017. [PDF] [Poster] [Code]
• Travis Ebesu, Yi Fang. Neural Semantic Personalized Ranking for Item Cold-Start Recommendation. In Information Retrieval Journal (IRJ), Springer, 2017. [PDF]