Sep 30, 2020 · We propose to select item features discriminately for different users. We study the personalization of feature selection at the level of the user or user group.

U-PFS introduces personalized feature weights and en- courages the sparsity of the weights by regularization;. G-PFS first clusters users into sub-groups and ...

Availing of high-dimensional item features, in this work, is opt for feature selection to solve the problem of recommending top-N new items and is effective ...

Oct 22, 2024 · Availing of high-dimensional item features, in this work, we opt for feature selection to solve the problem of recommending top-N new items.

The process of personalized feature selection filters out the dimensions that are irrelevant to recommendations or unappealing to users. Experimental results on ...

21 hours ago · Meta-learning addresses the cold-start problem by learning to quickly adapt a model to new items or users with minimal data. For example, (Zhu ...

In this paper, we propose a novel algorithmic framework based on matrix factorization that simultaneously exploits the similarity information among users and ...

Item/product cold start constitutes a problem mainly for collaborative filtering algorithms due to the fact that they rely on the item's interactions to make ...

This paper focuses on the item cold-start recommendation task. In the following subsec- tions, we briefly review three categories of the existing work relevant ...