Enforcing Topic Diversity in a Document Recommender for Conversations

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 superiority of the diverse merging technique over several others which not enforce the diversity of topics.


Presented at:
Proceedings of the Coling 2014 (25th International Conference on Computational Linguistics), Dublin, Ireland
Year:
2014
Laboratories:


Note: PRIVATE


 Record created 2014-06-19, last modified 2018-03-17

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