This paper aims to combine latent rating dimensions (such as those of latent-factor recommender systems) with latent review topics ( such as those learned by topic models like LDA), which more accurately predicts product ratings by harnessing the information present in review text.
Extensive empirical studies show that the proposed self-attention based sequential model (SASRec) outperforms various state-of-the-art sequential models (including MC/CNN/RNN-based approaches) on both sparse and dense datasets.
The approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within.
This paper builds novel models for the One-Class Collaborative Filtering setting, where the goal is to estimate users' fashion-aware personalized ranking functions based on their past feedback and combines high-level visual features extracted from a deep convolutional neural network, users' past feedback, as well as evolving trends within the community.
A novel machine learning task of identifying users' social circles is defined as a node clustering problem on a user's ego-network, a network of connections between her friends, and a model for detecting circles is developed that combines network structure as well as user profile information.
This paper proposes a scalable factorization model to incorporate visual signals into predictors of people's opinions, which is applied to a selection of large, real-world datasets and makes use of visual features extracted from product images using (pre-trained) deep networks.
This paper develops Communities from Edge Structure and Node Attributes (CESNA), an accurate and scalable algorithm for detecting overlapping communities in networks with node attributes that statistically models the interaction between the network structure and the node attributes, which leads to more accurate community detection as well as improved robustness in the presence of noise in thenetwork structure.
WaveGAN is a first attempt at applying GANs to unsupervised synthesis of raw-waveform audio, capable of synthesizing one second slices of audio waveforms with global coherence, suitable for sound effect generation.
The goal in this paper is to learn the semantics of substitutes and complements from the text of online reviews, trained using networks of products derived from browsing and co-purchasing logs and evaluated on the Amazon product catalog.
This work proposes an ‘extractive’ approach to identify review segments which justify users’ intentions and designs two personalized generation models which can generate diverse justifications based on templates extracted from justification histories.