Learning k-Determinantal Point Processes for Personalized …
In this work, we present a new optimization criterion LkP based on set probability comparison for personalized ranking that moves beyond traditional ranking-based methods. It for-malizes set-level relevance and diversity ranking comparisons through a Determinantal Point Process …
Learning 𝑘-Determinantal Point Processes for Personalized Ranking
The key to personalized recommendation is to predict a personalized ranking on a catalog of items by modeling the user’s preferences. There are many personalized ranking approaches …
Learning k-Determinantal Point Processes for Personalized Ranking
Jun 21, 2024 · In this work, we present a new optimization criterion LkP based on set probability comparison for personalized ranking that moves beyond traditional ranking-based methods.
Learning k-Determinantal Point Processes for Personalized Ranking
This paper presents a novel approach to personalized ranking using Determinantal Point Processes (DPPs). The key contribution is a method to learn the parameters of a k-DPP …
Learning k-Determinantal Point Processes for Personalized Ranking
The key to personalized recommendation is to predict a personalized ranking on a catalog of items by modeling the user's preferences. There are many personalized ranking approaches …
[1202.3738] Learning Determinantal Point Processes - arXiv.org
Feb 14, 2012 · Determinantal point processes (DPPs), which arise in random matrix theory and quantum physics, are natural models for subset selection problems where diversity is …
Learning k-Determinantal Point Processes for Personalized …
Learning k-Determinantal Point Processes for Personalized Ranking Yuli Liu , Christian Walder , Lexing Xie . In 40th IEEE International Conference on Data Engineering, ICDE 2024, …
k -DPPs: fixed-size determinantal point processes - ACM Digital …
Jun 28, 2011 · Determinantal point processes (DPPs) have recently been proposed as models for set selection problems where diversity is preferred. For example, they can be used to select …
based on set probability comparison for personalized ranking that moves beyond traditional ranking-based methods. It for-malizes set-level relevance and diversity ranking comparisons …
Enhancing Recommendation Diversity using Determinantal Point …
Jul 25, 2020 · In this paper, we focus on enhancing diversity of top-N recommendation, while ensuring the trade-off between accuracy and diversity. Thus, we propose an effective …