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Hidden factors and hidden topics: understanding rating dimensions with review text
TLDR
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.
Self-Attentive Sequential Recommendation
TLDR
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.
Image-Based Recommendations on Styles and Substitutes
TLDR
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.
Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering
TLDR
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.
Learning to Discover Social Circles in Ego Networks
TLDR
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.
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
TLDR
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.
Community Detection in Networks with Node Attributes
TLDR
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.
Adversarial Audio Synthesis
TLDR
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.
Inferring Networks of Substitutable and Complementary Products
TLDR
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.
Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects
TLDR
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.
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