| A Text Generation Model that Maintains the Order of Words, Topics, and Parts of Speech via Their Embedding Representations and Neural Language Models | 0 | 0.34 | 2021 |
| MARINE: Multi-relational Network Embeddings with Relational Proximity and Node Attributes | 2 | 0.38 | 2019 |
| Topic Structure-Aware Neural Language Model: Unified language model that maintains word and topic ordering by their embedded representations | 1 | 0.36 | 2019 |
| Topic Chronicle Forest for Topic Discovery and Tracking. | 0 | 0.34 | 2018 |
| Time Series Analysis Using NOC. | 0 | 0.34 | 2016 |
| N-gram over Context. | 2 | 0.51 | 2016 |
| Real Time Recommendations from Connoisseurs | 4 | 0.42 | 2015 |
| Supervised N-gram topic model | 6 | 0.42 | 2014 |
| Joint sentiment aspect model | 0 | 0.34 | 2012 |
| Identifying sentiments over N-gram | 3 | 0.39 | 2012 |
| Theme chronicle model: chronicle consists of timestamp and topical words over each theme | 2 | 0.37 | 2012 |
| Hierarchical Approach to Sentiment Analysis | 2 | 0.39 | 2012 |
| Predicting future reviews: sentiment analysis models for collaborative filtering | 9 | 0.61 | 2011 |
| Building a conversational model from two-tweets. | 6 | 0.72 | 2011 |
| Trend analysis model: trend consists of temporal words, topics, and timestamps | 23 | 1.08 | 2011 |
| Unsupervised Clustering Of Utterances Using Non-Parametric Bayesian Methods | 4 | 0.48 | 2011 |
| Latent interest-topic model: finding the causal relationships behind dyadic data | 9 | 0.63 | 2010 |
| Serendipitous recommendations via innovators | 19 | 0.75 | 2010 |
| Trend detection model | 5 | 0.51 | 2010 |
| Author interest topic model | 8 | 0.56 | 2010 |
| Personalized recommendation based on the personal innovator degree | 12 | 0.63 | 2009 |
| Information retrieval based on collaborative filtering with latent interest semantic map | 2 | 0.39 | 2005 |
