Paper: Enforcing Topic Diversity in a Document Recommender for Conversations

ACL ID C14-1056
Title Enforcing Topic Diversity in a Document Recommender for Conversations
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

This paper addresses the problem of building concise, diverse and relevant lists of documents, which can be recommended to the participants of a conversation to fulfill their information needs without distracting them. These lists are retrieved periodically by submitting multiple implicit queries derived from the pronounced words. Each query is related to one of the topics identified in the conversation fragment preceding the recommendation, and is submitted to a search engine over the English Wikipedia. We propose in this paper an algorithm for diverse merging of these lists, using a submodular reward function that rewards the topical similarity of documents to the conversation words as well as their diversity. We evaluate the proposed method through crowdsourcing. The results show the su...