Titlebook: Knowledge Science, Engineering and Management; 13th International C Gang Li,Heng Tao Shen,Xiang Zhao Conference proceedings 2020 Springer N

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Parameter Optimization and Weights Assessment for Evidential Artificial Immune Recognition Systemical elements. They achieved a big success in the area of machine learning. Nevertheless, the majority of AIRS versions does not take into account the effect of uncertainty related to the classification process. Managing uncertainty is undoubtedly among the fundamental challenges in real-world class

indigenous

概观

Itinerant

Pairwise-Based Hierarchical Gating Networks for Sequential Recommendationcommendation task. Most existing methods based on Markov Chains or deep learning architecture have demonstrated their superiority in sequential recommendation scenario, but they have not been well-studied at a range of problems: First, the influence strength of items that the user just access might

START

Time-Aware Attentive Neural Network for News Recommendation with Long- and Short-Term User Representtations is a challenging task in news recommendation. Existing methods usually utilize recurrent neural networks to capture the short-term user interests, and have achieved promising performance. However, existing methods ignore the user interest drifts caused by time interval in the short session.

易发怒

A Time Interval Aware Approach for Session-Based Social Recommendationtheir interests to enhance the activeness and retention of users. Besides, their interests change from time to time. Session-based recommendation divides users’ interaction history into sessions and predict users’ behaviors with the context information in each session. It’s essential but challenging

moratorium

AutoIDL: Automated Imbalanced Data Learning via Collaborative Filteringthods usually ignore the intrinsic imbalance nature of most real-world datasets and lead to poor performance. For handling imbalanced data, sampling methods have been widely used since their independence of the used algorithms. We propose a method named AutoIDL for selecting the sampling methods as

保守

Fusion of Domain Knowledge and Text Features for Query Expansion in Citation Recommendationortant for literature reviewing, literature-based discovery and a wide range of applications. In this paper, we propose a query expansion framework via fusing domain-specific knowledge and text features for academic citation recommendation. Starting from an original query, domain-specific and contex