Context-aware Image Tweet Modelling and Recommendation

86 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Neural Collaborative Filtering (2017, background): “Moreover, we are particularly interested in building recommender systems for multi-media items, an interesting task but has received relatively less scrutiny in the recommendation community [3].”
Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention (2017, background): “significance of multimedia recommendation has led to the great attention from both the industry and academia [8, 16, 32, 36].”
Attributed Social Network Embedding (2017, background): “images in Weibo [62], making it urgent and meaningful to”
EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction (2019, background): “It has brought a plethora 150 of successful applications in industry, and has yielded great promise in a variety of prediction tasks, such as regression, classification and ranking (Oentaryo et al., 2014; Chen et al., 2016; Juan et al., 2016; Bayer et al., 2017).”
MMALFM (2018, background): “Chen et al. [11] utilize visual features for personalized image tweet recommendation.”