Quote Recommendation in Dialogue using Deep Neural Network

By Hanbit Lee, Yeonchan Ahn, Haejun Lee, Seungdo Ha, Sang-goo Lee. 2016

51 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations (2020, background): “Lee et al.[14] proposed a hybrid model that integrates RNNs with CNNs for quotes recommendation.”
Deep Learning Based Recommender System (2017, methodology): “[82] proposed a deep hybrid model with RNNs and CNNs for quote recommendation.”
Recommendation system based on deep learning methods: a systematic review and new directions (2019, methodology): “For example, Zhang et al. (2016a) and Lin et al. (2015) integrated AE with CNN to extract visual and textual contents using the word embedding approach for better representation learning to improve the predictive performance of RS. Lee et al. (2016) combined CNN and RNN for quote RS.”
A Survey on Conversational Recommender Systems (2020, methodology): “antic distance between entities and keywords mentioned in the conversation [71,78]. Wikiquote A quote dataset crawled from two online sources, Wikiquote8 and the Oxford Concise Dictionary of Proverbs [68]. Citysearch In [78], a dataset of 137,000 users reviews on 24,000 restaurants was harvested fro”
A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “Recently, researchers proposed to leverage word embedding techniques for better content recommendation [69], [82], [83], [84], [85], [86], [87], [88].”