Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Zhang, Shenzheng"'
Publikováno v:
ORSUM@ACM RecSys 2023, 6th Workshop on Online Recommender Systems and User Modeling, jointly with the 17th ACM Conference on Recommender Systems, September 19th, 2023, Singapore
Recommender systems usually leverage multi-task learning methods to simultaneously optimize several objectives because of the multi-faceted user behavior data. The typical way of conducting multi-task learning is to establish appropriate parameter sh
Externí odkaz:
http://arxiv.org/abs/2309.10357
The gap between the randomly initialized item ID embedding and the well-trained warm item ID embedding makes the cold items hard to suit the recommendation system, which is trained on the data of historical warm items. To alleviate the performance de
Externí odkaz:
http://arxiv.org/abs/2302.14395
We see widespread adoption of slate recommender systems, where an ordered item list is fed to the user based on the user interests and items' content. For each recommendation, the user can select one or several items from the list for further interac
Externí odkaz:
http://arxiv.org/abs/2302.12427
Embedding & MLP has become a paradigm for modern large-scale recommendation system. However, this paradigm suffers from the cold-start problem which will seriously compromise the ecological health of recommendation systems. This paper attempts to tac
Externí odkaz:
http://arxiv.org/abs/2205.13795