QuoteRec: Toward Quote Recommendation for Writing

Quote is a language phenomenon of transcribing the statement of someone else, such as a proverb and a famous saying. An appropriate usage of quote usually equips the expression with more elegance and credibility. However, there are times when we are eager to stress our idea by citing a quote, while nothing relevant comes to mind. Therefore, it is exciting to have a recommender system which provides quote recommendations while we are writing. This article extends previous study of quote recommendation, the task that recommends the appropriate quote according to the context (i.e., the content occurring before and after the quote). In this article, a quote recommender system called QuoteRec is presented to tackle the task. We investigate two models to learn the vector representations of quotes and contexts, and then rank the candidate quotes based on the representations. The first model learns the quote representation according to the contexts of a quote. The second model is an extension of the neural network model in previous study, which learns the representation of a quote by concerning both its content and contexts. Experimental results demonstrate the effectiveness of the two models in learning the semantic representations of quotes, and the neural network model achieves state-of-the-art results on the quote recommendation task.