Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Generalized vector spaces model in information retrieval
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
ACM Computing Surveys (CSUR)
Stable algorithms for link analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Improving personalized web search using result diversification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
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Despite the explosive growth of internet usage, satisfying users' experience in web search while understanding the dynamic nature of search engine ranking remains a challenge. The main reason is that user queries and web documents may belong to different categories given a taxonomy of information. In this study, we attempt to detangle the ambiguity in web search by using a systematic approach. A weighted graph model is utilised to represent the complicated documents network from web search results. In this model, document topics are explicitly defined from the weighted document graph with an unsupervised learning method. On weighing each topic, both the size of the topic and the intra-topic document importance distribution are considered. In order to achieve web search results diversity, the reranking method proposed in this work leverages on the relevance of the documents to the query and related topics. Evaluation results on synthetic data and realistic web pages show the efficacy of a proposed system.