Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Retrieval evaluation with incomplete information
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
Improving similarity measures for short segments of text
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Incorporating user behaviors in new word detection
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Similarity measures for short segments of text
ECIR'07 Proceedings of the 29th European conference on IR research
Why press backspace?: understanding user input behaviors in Chinese Pinyin input method
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
User Behaviors in Related Word Retrieval and New Word Detection: A Collaborative Perspective
ACM Transactions on Asian Language Information Processing (TALIP)
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Motivated by Google Sets, we study the problem of growing related words from a single seed word by leveraging user behaviors hiding in user records of Chinese input method. Our proposed method is motivated by the observation that the more frequently two words co-occur in user records, the more related they are. First, we utilize user behaviors to generate candidate words. Then, we utilize search engine to enrich candidate words with adequate semantic features. Finally, we reorder candidate words according to their semantic relatedness to the seed word. Experimental results on a Chinese input method dataset show that our method gains better performance.