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Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation
2021
The Journal of Artificial Intelligence Research
In this paper, we propose a theoretically supported sequential strategy for training a large-scale Recommender System (RS) over implicit feedback, mainly in the form of clicks. The proposed approach consists in minimizing pairwise ranking loss over blocks of consecutive items constituted by a sequence of non-clicked items followed by a clicked one for each user. We present two variants of this strategy where model parameters are updated using either the momentum method or a gradient-based
doi:10.1613/jair.1.12562
fatcat:npcypmwkfrasnpfa2iw42dq2jy