Prior-Based Dual Additive Latent Dirichlet Allocation for User-Item Connected Documents

AAAI Conferences

User-item connected documents, such as customer reviews for specific items in online shopping website and user tips in location-based social networks, have become more and more prevalent recently. Inferring the topic distributions of user-item connected documents is beneficial for many applications, including document classification and summarization of users and items. While many different topic models have been proposed for modeling multiple text, most of them cannot account for the dual role of user-item connected documents (each document is related to one user and one item simultaneously) in topic distribution generation process. In this paper, we propose a novel probabilistic topic model called Prior-based Dual Additive Latent Dirichlet Allocation (PDA-LDA). It addresses the dual role of each document by associating its Dirichlet prior for topic distribution with user and item topic factors, which leads to a document-level asymmetric Dirichlet prior. In the experiments, we evaluate PDA-LDA on several real datasets and the results demonstrate that our model is effective in comparison to several other models, including held-out perplexity on modeling text and document classification application.


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Score more than 50 hours of training in machine learning for less than $40

Mashable

Computers may not wear tennis shoes (yet), but thanks to developing artificial intelligence technologies, they're smarter than ever before. Along with those technologies has come a relatively new category of computer science called machine learning, or ML. Similar to statistics, ML involves computer systems that utilize algorithms to automatically learn about data, recognize patterns, and make decisions, all without outside intervention or explicit directions from human beings. In the real world, you can find it being used in smart assistants like Siri and the Amazon Echo, in online fraud detection services, in the facial recognition feature that identifies photos of you on Facebook, and more recently, in Tesla's self-driving car. ML is distinctive in the world of AI in that it can be used to process vast amounts of data quickly, making it a desirable tech skill among job applicants not only in the fields of computer science and engineering, but also marketing, health care, finance, social media, and beyond.