Hao Lu

TL;DR: In this article, a sentiment analysis method for micro-blog short text is presented, which comprises the following steps: step 1, collecting microblog data including keywords including keywords so as to store in a database; step 2, pre-processing the microblogs data; step 3, loading associated dictionaries; step 4, processing sentence division and filtering sentences which do not include user configuration keywords; step 5, processing word division to the sentences including the keywords and labeling parts of speech; step 6, processing dependency sentence structure analysis to sentences including subjects by a sentence structure analyzing tool;

TL;DR: A hybrid recommendation model by fusing network structured feature with graph neural networks and user interactive activities with tensor factorization was proposed, which outperforms other existing neural network and matrix factorization models including xSVD++, RTTF and DSE with a smaller predictive error as well as better recommendation accuracy.

TL;DR: A recurrent neural network with an attention mechanism is built, capable of obtaining users’ preferences in the current session and consequently making recommendations, which outperforms the current state-of-the-art short-term music recommendation systems on one real-world dataset.

TL;DR: In this article, a heterogeneous knowledge embedding-based attentive RNN model is proposed to recommend scientific paper citations, which is based on the implicit influence of scholars' previous preferences of writing and citing on his/her new manuscript.

TL;DR: This paper proposes a novel way to forecast and generate alerts for city-level traffic incidents based on a social approach rather than traditional physical approaches and considers the news report as an objective measurement to flexibly validate the feasibility of proposed model from social cyberspace to physical space.