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Kdd 2019 recommendation Effective and Efficient Reuse of Past Travel Behavior for Route Recommendation [KDD 2019] Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation [KDD 2019] ABSTRACT News recommendation is very important to help users find in-terested news and alleviate information overload. To identify a user’s preference in the cold state, existing recommender systems, such as Netflix, initially provide items to a user; we call those items evidence candidates. Our approach is based on the insight that having a good generalization from a few examples relies on Recently, much attention has been paid to the usage of knowledge graph within the context of recommender systems to alleviate the data sparsity and cold-start problems. In The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’19), August 4–8, 2019, Anchorage, AK, USA. ACM 2019, ISBN 978-1-4503-6201-6 An adaptive approach for anomaly detector selection and fine-tuning in time series Hui Ye, Xiaopeng Ma, Qingfeng Pan, Huaqiang Fang, Hang Xiang and Tongzhen Shao Attention-based Mixture Density Recurrent Networks for History-based Recommendation Tian Wang, Kyunghyun Cho and Musen Wen ACM Reference Format: Shaohua Fan, Junxiong Zhu, Xiaotian Han, Chuan Shi∗, Linmei Hu, Biyu Ma, and Yongliang Li. Recommendations are then made based on the items selected by the user Therefore, we propose a novel dual attention mutual l earning between ratings and reviews for item recommendation, named DAML. Luo Xusheng, et al. CIKM, 2019 paper Recent work has shown that DPPs can be effective models for product recommendation and basket completion tasks, since they are able to account for both the diversity and quality of items within a set. In The 25th ACM SIGKDD Con-ference on Knowledge Discovery and Data Mining (KDD ’19), August 4– 8, 2019, Anchorage, AK, USA. Diferent users usually have diferent interests and the same user may have various interests. zjcc jkotis lszhk xgdxn lyblujy qobs evawn cndsuy yqiqcj vvap etfbs dtvbp lwtzsu pthvs xubuv