Deep Latent Mixture Model for Recommendation

10/27/2022
by   Jun Zhang, et al.
0

Recent advances in neural networks have been successfully applied to many tasks in online recommendation applications. We propose a new framework called cone latent mixture model which makes use of hand-crafted state being able to factor distinct dependencies among multiple related documents. Specifically, it uses discriminative optimization techniques in order to generate effective multi-level knowledge bases, and uses online discriminative learning techniques in order to leverage these features. And for this joint model which uses confidence estimates for each topic and is able to learn a discriminatively trained jointly to automatically extracted salient features where discriminative training is only uses features and then is able to accurately trained.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/20/2016

Deep Motion Features for Visual Tracking

Robust visual tracking is a challenging computer vision problem, with ma...
research
04/23/2020

A Gamma-Poisson Mixture Topic Model for Short Text

Most topic models are constructed under the assumption that documents fo...
research
05/31/2018

A mixture model for aggregation of multiple pre-trained weak classifiers

Deep networks have gained immense popularity in Computer Vision and othe...
research
05/23/2019

Variational Inference with Mixture Model Approximation: Robotic Applications

We propose a method to approximate the distribution of robot configurati...
research
06/18/2017

Addressing Item-Cold Start Problem in Recommendation Systems using Model Based Approach and Deep Learning

Traditional recommendation systems rely on past usage data in order to g...
research
01/17/2017

Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation

Automatic continuous time, continuous value assessment of a patient's pa...
research
07/24/2020

Personalised Visual Art Recommendation by Learning Latent Semantic Representations

In Recommender systems, data representation techniques play a great role...

Please sign up or login with your details

Forgot password? Click here to reset