This paper considers how to design coordination strategies to achieve the coordination in decentralized supply chain, considering pricing, inventory and transportation cost simultaneously under the price sensitive env...
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ISBN:
(纸本)9781424425020
This paper considers how to design coordination strategies to achieve the coordination in decentralized supply chain, considering pricing, inventory and transportation cost simultaneously under the price sensitive environment. Based on a supplier-Stackelberg game structure, we analyze the non-cooperation decision model and design the algorithm. Then we propose the cooperation decision model and design the algorithm. Revenue sharing and price discount strategies are used to coordinate the decentralized supply chain. The experimental results show that both revenue sharing and price discount strategy can efficiently make the coordination of the supplier and the retailer.
Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users’ preferences f...
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Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users’ preferences for music moods. However, existing emotion-aware music recommender systems (EMRSs) explicitly or implicitly assume that users’ actual emotional states expressed through identical emotional words are homogeneous. They also assume that users’ music mood preferences are homogeneous under the same emotional state. In this article, we propose four types of heterogeneity that an EMRS should account for: emotion heterogeneity across users, emotion heterogeneity within a user, music mood preference heterogeneity across users, and music mood preference heterogeneity within a user. We further propose a Heterogeneity-aware Deep Bayesian Network (HDBN) to model these assumptions. The HDBN mimics a user’s decisionprocess of choosing music with four components: personalized prior user emotion distribution modeling, posterior user emotion distribution modeling, user grouping, and Bayesian neural network-based music mood preference prediction. We constructed two datasets, called EmoMusicLJ and EmoMusicLJ-small, to validate our method. Extensive experiments demonstrate that our method significantly outperforms baseline approaches on metrics of HR, Precision, NDCG, and MRR. Ablation studies and case studies further validate the effectiveness of our HDBN. The source code and datasets are available at https://***/jingrk/HDBN.
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