Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Mining search engine query logs for query recommendation
Proceedings of the 15th international conference on World Wide Web
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
Scalable near identical image and shot detection
Proceedings of the 6th ACM international conference on Image and video retrieval
Novelty detection for cross-lingual news stories with visual duplicates and speech transcripts
Proceedings of the 15th international conference on Multimedia
LDA-Based Retrieval Framework for Semantic News Video Retrieval
ICSC '07 Proceedings of the International Conference on Semantic Computing
Modeling Semantic Aspects for Cross-Media Image Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of phrasal query suggestions
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Query suggestions using query-flow graphs
Proceedings of the 2009 workshop on Web Search Click Data
Multilayer pLSA for multimodal image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Organizing query completions for web search
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Generating phrasal and sentential paraphrases: A survey of data-driven methods
Computational Linguistics
Query suggestions in the absence of query logs
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
| Hi-index | 0.00 |
Query suggestion is an assistive technology mechanism commonly used in search engines to enable a user to formulate their search queries by predicting or completing the next few query words that the user is likely to type. In most implementations, the suggestions are mined from query log and use some simple measure of query similarity such as query frequency or lexicographical matching. In this paper, we propose an alternative method of presenting query suggestions by their thematic topics. Our method adopts a document-centric approach to mine topics in the corpus, and does not require the availability of a query log. The heart of our algorithm is a probabilistic topic model that assumes that topics are multinomial distributions of words, and jointly learns the co-occurrence of textual words and the visual information in the video stream. Empirical results show that this alternate way of organizing query suggestions can better elucidate the high level query intent, and more effectively help a user meet his information need.