Matches in ScholarlyData for { ?s ?p ?o. }
- 740 title "Do you want to take notes? Identifying research missions in Yahoo! Search Pad".
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- item-1 hasContent debora-donato.
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- item-2 hasContent francesco-bonchi.
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- item-3 hasContent tom-chi.
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- item-4 hasContent yoelle-maarek.
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- 763 creator li-wei-he.
- 763 creator yang-song.
- 763 type InProceedings.
- 763 label "Optimal Rare Query Suggestion With Implicit User Feedback".
- 763 sameAs 763.
- 763 abstract "Query suggestion has been an effective approach to help users narrow down to the information they need. However, most of existing studies focused on only popular/head queries. Since rare queries possess much less information (e.g., clicks) than popular queries in the query logs, it is much more difficult to efficiently suggest relevant queries to a rare query. In this paper, we propose an optimal rare query suggestion framework by leveraging implicit feedbacks from users in the query logs. Our model resembles the principle of pseudo-relevance feedback which assumes that top-returned results by search engines are relevant. However, we argue that the clicked URLs and skipped URLs contain different levels of information and thus should be treated differently. Hence, our framework optimally combines both the click and skip information from users and uses a random walk model to optimize the query correlation. Our model specifically optimizes two parameters: (1) the restarting (jumping) rate of random walk, and (2) the combination ratio of click and skip information. Unlike the Rocchio algorithm, our learning process does not involve the content of the URLs but simply leverages the click and skip counts in the query-URL bipartite graphs. Consequently, our model is capable of scaling up to the need of commercial search engines. Experimental results on one-month query logs from a large commercial search engine with over 40 million rare queries demonstrate the superiority of our framework, with statistical significance, over the traditional random walk models and pseudo-relevance feedback models.".
- 763 hasAuthorList authorList.
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- 763 keyword "Query processing".
- 763 keyword "diversity".
- 763 keyword "ranking".
- 763 keyword "relevance".
- 763 keyword "retrieval".
- 763 title "Optimal Rare Query Suggestion With Implicit User Feedback".
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- item-1 hasContent yang-song.
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- 777 creator anlei-dong.
- 777 creator bai-jing.
- 777 creator fernando-diaz.
- 777 creator hongyuan-zha.
- 777 creator pranam-kolari.
- 777 creator ruiqiang-zhang.
- 777 creator yi-chang.
- 777 creator zhaohui-zheng.
- 777 type InProceedings.
- 777 label "Use Twitter Data for Recency Ranking Improvement in Web Search".
- 777 sameAs 777.
- 777 abstract "Twitter is a social network and a micro-blogging service, which becomes very popular nowadays. People use Twitter to exchange messages, which contain fresh and useful information. This paper proposes a ranking system for web search which utilizes Twitter data to improve ranking results, especially to improve the freshness of ranking results. We treat the urls that were ever referred by Twitter users (called as Twitter urls) differently compared with regular urls. A challenging problem for Twitter urls is that they lack click information and anchor-text information due to their freshness, which restrict them from being promoted appropriately in ranking results. We analyze the unique characteristics within the twitter microcosm such as Twitter users’ following relationship and the texts of tweets, and we use them as new evidences for ranking Twitter urls appropriately in web search. We then use a compositional modeling algorithm to fully use the available data and different categories of rank features. This approach solves the dilemma in recency ranking that fresh documents cannot be promoted appropriately due to the lack of favorable rank features that need to be aggregated over time. To evaluate ranking results, we not only incorporate recency demotion into discounted cumulative grade (DCG) for stale documents, but also use discounted cumulative freshness (DCF) to evaluate the most fresh documents in ranking results. The efficacy of this approach is illustrated by the experiments on real data.".
- 777 hasAuthorList authorList.
- 777 isPartOf proceedings.
- 777 keyword "Use of social metadata".
- 777 keyword "annotations in search".
- 777 keyword "mining".
- 777 title "Use Twitter Data for Recency Ranking Improvement in Web Search".
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- item-1 hasContent anlei-dong.
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- item-6 hasContent fernando-diaz.
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