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• item-2 hasContent jiang-dejun.
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• item-3 hasContent guillaume-pierre.
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• item-4 hasContent chi-hung-chi.
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• item-5 hasContent maarten-van-steen.
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• 153 creator ivan-titov.
• 153 creator ryan-mcdonald.
• 153 type InProceedings.
• 153 label "Modeling Online Reviews with Multi-grain Topic Models".
• 153 sameAs 153.
• 153 abstract "In this paper we present a novel framework for extracting the ratable aspects of objects from online user reviews. Extracting such aspects is an important challenge in automatically mining product opinions from the web and in generating opinion-based summaries of user reviews. Our models are based on extensions to standard topic modeling methods such as LDA and PLSA to induce multi-grain topics. We argue that multi-grain models are more appropriate for our task since standard models tend to produce topics that correspond to global properties of objects (e.g., the brand of a product type) rather than the aspects of an object that tend to be rated by a user. The models we present not only extract ratable aspects, but also cluster them into coherent topics, e.g., ``waitress'' and ``bartender'' are part of the same topic ``staff'' for restaurants. This differentiates it from much of the previous work which extracts aspects through term frequency analysis with minimal clustering. We evaluate the multi-grain models both qualitatively and quantitatively to show that they improve significantly upon standard topic models.".
• 153 hasAuthorList authorList.
• 153 hasTopic World_Wide_Web.
• 153 isPartOf proceedings.
• 153 keyword "clustering".
• 153 keyword "opinion mining".
• 153 keyword "topic models".
• 153 title "Modeling Online Reviews with Multi-grain Topic Models".
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• item-1 hasContent ivan-titov.
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• item-2 hasContent ryan-mcdonald.
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• 155 creator hai-jin.
• 155 creator hanhua-chen.
• 155 creator lei-chen.
• 155 creator lionel-ni.
• 155 creator yunhao-liu.
• 155 type InProceedings.
• 155 label "Efficient Multi-keyword Search over P2P Web".
• 155 sameAs 155.
• 155 abstract "Current search mechanisms of DHT-based P2P systems can well handle a single keyword search problem. Other than single key word search, multi-keyword search is quite popular and useful in many real applications. Simply using the solution for single key word search will require distributed intersection/union operations in wide area networks, leading to unacceptable traffic cost. As it is well known that Bloom Filter (BF) is effective in reducing traffic, we would like to use BF encoding to handle multi-keyword search. Applying BF is not difficult, but how to get optimal results is not trivial. In this study we show, through mathematical proof, that the optimal setting of BF in terms of traffic cost is determined by the global statistical information of keywords, not the false positive rate as claimed by previous methods. Through extensive experiments, we demonstrate how to obtain optimal settings. We further argue that the intersection order between sets is important for multi-keyword search. Thus, we design optimal order strategies based on BF for both "and" and "or" queries. To better evaluate the performance of this design, we conduct extensive simulations on TREC WT10G test collection and the query log of MSN search engine. Results show that our design significantly reduces the search traffic and latency of existing approach by 73.03% and 41.37%, respectively.".
• 155 hasAuthorList authorList.
• 155 hasTopic World_Wide_Web.
• 155 isPartOf proceedings.
• 155 keyword "Bloom Filter".
• 155 keyword "DHT".
• 155 keyword "Multi-keyword Search".
• 155 keyword "P2P".
• 155 title "Efficient Multi-keyword Search over P2P Web".
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• item-1 type ListItem.
• item-1 hasContent hanhua-chen.
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• item-2 hasContent hai-jin.
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• item-3 hasContent lei-chen.
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• item-4 hasContent yunhao-liu.
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• item-5 hasContent lionel-ni.
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• 175 creator hamed-valizadegan.
• 175 creator hang-li.
• 175 creator rong-jin.
• 175 type InProceedings.
• 175 label "Ranking Refinement and Its Application to Information Retrieval".
• 175 sameAs 175.
• 175 abstract "We consider the problem of ranking refinement, i.e., to improve the accuracy of an existing ranking function with a small set of labeled instances. We are, particularly, interested in learning a better ranking function using two complementary sources of information, ranking information given by the existing ranking function (i.e., a base ranker) and that obtained from users’ feedbacks. This problem is very important in information retrieval where the feedbacks are gradually collected. The key challenge in combining the two sources of information arises from the fact that the ranking information presented by the base ranker tends to be imperfect and the ranking information obtained from users’ feedbacks tends to be insufficient. We present a novel boosting framework for ranking refinement that can effectively leverage the uses of the two sources of information. Our empirical study shows that the proposed algorithm is effective for ranking refinement, and furthermore significantly outperforms the state-of-the-arts ranking algorithms that incorporate the output from the base ranker as an additional feature of instance.".
• 175 hasAuthorList authorList.
• 175 hasTopic World_Wide_Web.
• 175 isPartOf proceedings.
• 175 keyword "Feedback data".
• 175 keyword "Information Retrieval".
• 175 keyword "Learning to Rank".
• 175 title "Ranking Refinement and Its Application to Information Retrieval".
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