University of California recommends NextSmarty’s research paper

The University of California is using NextSmarty’s research paper as course reading

The University of California in San Diego is using NextSmary’s research paper in their bibliography for the Computer Science graduate course of Trends in Recommender Systems and Human Behavioral Modeling.

Our research paper is downloadable from the UC’s website:

3D Convolutional Networks for Session-based Recommendation with Content Features (Tuan and Phuong, 2017).

In 2017, we were presenting this research publication at the major recommender system conference RecSys that was held in Como (Italy), and the world leader in retargeting marketing Criteo featured it in their Top 10.

Other readings used in UC course

We are glad to see that other readings in the course material are sponsored by major companies and universities:

  • Research at Google

“Recurrent Recommender Networks” by Research at Google, the authors propose an RRN system that is based on a Long-Short Term Memory. They claim that their engine is able to predict user’s future behavioral trajectories.

  • University of Duisburg Essen

“Sequential User-based Recurrent Neural Network Recommendations” by Univerity of Duisburg Essen, considering unique characteristics of the Recommender Systems domain, they show “how individual users can be represented in addition to sequences of consumed items in a new type of Gated Recurrent Unit to effectively produce personalized next item recommendations.”

  • Microsoft Research

Collaborative Knowledge Base Embedding for Recommender Systems by Microsoft Research, the authors “investigate how to leverage the heterogeneous information in a knowledge base to improve the quality of recommender systems” to address the problems of limited performances in collaborative filtering.

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