An Intelligent Customization Framework for Tourist Trip Design Problems

In the era of the experience economy, “customized tours” and “self-guided tours” have become mainstream. This paper proposes an end-to-end framework for solving the tourist trip design problems (TTDP) using deep reinforcement learning (DRL) and data analysis. The proposed approach considers heterogeneous tourist preferences, customized requirements, and stochastic traffic times in real applications. With various heuristics methods, our approach is scalable without retraining for every new problem instance, which can automatically adapt the solution when the problem constraint changes slightly. We aim to provide websites or users with software tools that make it easier to solve TTDP, promoting the development of smart tourism and customized tourism

Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections

In this paper, we propose a novel low-tubal-rank tensor recovery model, which directly constrains the tubal rank prior for effectively removing the mixed Gaussian and sparse noise in hyperspectral images. The constraints of tubal-rank and sparsity can govern the solution of the denoised tensor in the recovery procedure. To solve the constrained low-tubal-rank model, we develop an iterative algorithm based on bilateral random projections to efficiently solve the proposed model. The advantage of random projections is that the approximation of the low-tubal-rank tensor can be obtained quite accurately in an inexpensive manner. Experimental examples for hyperspectral image denoising are presented to demonstrate the effectiveness and efficiency of the proposed method.Comment: Accepted by IGARSS 201

Behavior-Driven Model Design: A Deep Learning Recommendation Model Jointing Users and Products Reviews

Data-driven is widely mentioned, but the data is generated by user behavior. Our work aims to utilize a behavior-driven model design pattern to improve accuracy and provide explanations in review-based recommendations. Review-based recommendation introduces review text to overcome the sparseness and unexplainably of rating or scores-based model. Driven by users rating behavior and human cognitive abilities, we proposed a deep learning recommendation model jointing users and products reviews (DLRM-UPR) to learn user preferences and product characteristics adaptively. The DLRM-UPR consists of word, text, and context co-attention layers considering the interaction between each user-product-context pair. Extensive experiments on real datasets demonstrate that DLRM-UPR outperforms existing state-of-the-art models. In addition, the relevant information in the reviews and the suggestion for improving the user experience can be highlighted to explain the recommendation results