Multi-objective dynamic programming with limited precision

Mandow, Lawrence; Pérez-de-la-Cruz, José-Luís; Pozas, Nicolás

doi:10.1007/s10898-021-01096-x

Cited by 4 publications

(1 citation statement)

References 23 publications

(39 reference statements)

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“…Collaborative filtering, also called social filtering, provides users with personalized service and filtering through similarity principle. The similarity principle of collaborative filtering holds that if users' ideological level, values, and knowledge level are similar, then they will have the same or similar information needs [13]. In a very large optional space, any system that can output personalized recommendations or provide a method to accurately guide users to find information they are interested in or useful [14].…”

Section: Introductionmentioning

confidence: 99%

Precision Marketing Optimization Model of e-Commerce Platform Based on Collaborative Filtering Algorithm

2022

Wireless Communications and Mobile Computing

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e-commerce mode shows great modern commercial value. In particular, online shopping has become a fashion and trend for people because of its convenience and rapidness. How to find the information users that need accurately and quickly in the increasing network information and recommend products is a big problem. Although precision marketing was mainly used in e-commerce activities in the past, due to factors such as the technical basis and data analysis ability at that time, there was not enough technical ability and theoretical basis to deeply mine and make use of the existing data. The collaborative filtering algorithm is one of the most widely used and successful recommendation techniques, but it has obvious defects. In this paper, the nearest neighbor collaborative filtering recommendation algorithm based on statistical eigenvalue classification is proposed in the collaborative filtering algorithm. By calculating the similarity between items, the user’s rating of unrated items is preliminarily predicted, the nearest neighbor of items is formed, and the classified cluster of items is formed. The matrix is filled by the similarity between related items. The cold treatment problem is solved under the optimization of the ant colony algorithm. In the experiment of the model, the optimization rate for the cold start problem is 87.3%.

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Section: Introductionmentioning

confidence: 99%

Precision Marketing Optimization Model of e-Commerce Platform Based on Collaborative Filtering Algorithm

2022

Wireless Communications and Mobile Computing

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Asynchronous Vector Iteration in Multi-objective Markov Decision Processes

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Mandow

Pérez-de-la-Cruz

2021

Advances in Artificial Intelligence

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Multi-Objective Decision-Making Meets Dynamic Shortest Path: Challenges and Prospects

2023

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The Shortest Path (SP) problem resembles a variety of real-world situations where one needs to find paths between origins and destinations. A generalization of the SP is the Dynamic Shortest Path (DSP) problem, which also models changes in the graph at any time. When a graph changes, DSP algorithms partially recompute the paths while taking advantage of the previous computations. Although the DSP problem represents many real situations, it leaves out some fundamental aspects of decision-making. One of these aspects is the existence of multiple, potentially conflicting objectives that must be optimized simultaneously. Recently, we performed a first incursion on the so-called Multi-Objective Dynamic Shortest Path (MODSP), presenting the first algorithm able to take the MODM perspective into account when solving a DSP problem. In this paper, we go beyond and formally define the MODSP problem, thus establishing and clarifying it with respect to its simpler counterparts. In particular, we start with a brief overview of the related literature and then present a complete formalization of the MODSP problem class, highlighting its distinguishing features as compared to similar problems and representing their relationship through a novel taxonomy. This work also motivates the relevance of the MODSP problem by enumerating real-world scenarios that involve all its ingredients, such as multiple objectives and dynamically updated graph topologies. Finally, we discuss the challenges and open questions for this new class of shortest path problems, aiming at future work directions. We hope this work sheds light on the theme and contributes to leveraging relevant research on the topic.

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Multi-objective dynamic programming with limited precision

Cited by 4 publications

References 23 publications

Precision Marketing Optimization Model of e-Commerce Platform Based on Collaborative Filtering Algorithm

Precision Marketing Optimization Model of e-Commerce Platform Based on Collaborative Filtering Algorithm

Asynchronous Vector Iteration in Multi-objective Markov Decision Processes

Multi-Objective Decision-Making Meets Dynamic Shortest Path: Challenges and Prospects

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