Search Results for author: Philippe Fournier-Viger

Found 4 papers, 0 papers with code

Towards Target High-Utility Itemsets

no code implementations9 Jun 2022 Jinbao Miao, Wensheng Gan, Shicheng Wan, Yongdong Wu, Philippe Fournier-Viger

In this paper, we address this issue by proposing a novel list-based algorithm with pattern matching mechanism, named THUIM (Targeted High-Utility Itemset Mining), which can quickly match high-utility itemsets during the mining process to select the targeted patterns.

Discovering Representative Attribute-stars via Minimum Description Length

no code implementations27 Apr 2022 Jiahong Liu, Min Zhou, Philippe Fournier-Viger, Menglin Yang, Lujia Pan, Mourad Nouioua

However, there are generally two limitations that hinder their practical use: (1) they have multiple parameters that are hard to set but greatly influence results, (2) and they generally focus on identifying complex subgraphs while ignoring relationships between attributes of nodes. Graphs are a popular data type found in many domains.

Decision Making

Towards Revenue Maximization with Popular and Profitable Products

no code implementations26 Feb 2022 Wensheng Gan, Guoting Chen, Hongzhi Yin, Philippe Fournier-Viger, Chien-Ming Chen, Philip S. Yu

To fulfill this gap, in this paper, we first propose a general profit-oriented framework to address the problem of revenue maximization based on economic behavior, and compute the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.

Marketing

Enhancing Hyperbolic Graph Embeddings via Contrastive Learning

no code implementations21 Jan 2022 Jiahong Liu, Menglin Yang, Min Zhou, Shanshan Feng, Philippe Fournier-Viger

Inspired by the recently active and emerging self-supervised learning, in this study, we attempt to enhance the representation power of hyperbolic graph models by drawing upon the advantages of contrastive learning.

Contrastive Learning Graph Representation Learning +2

Cannot find the paper you are looking for? You can Submit a new open access paper.