Matches in ScholarlyData for { ?s ?p ?o. }
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- item-3 hasContent manish-gupta.
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- 711 creator chin-yew-lin.
- 711 creator huizhong-duan.
- 711 creator yunbo-cao.
- 711 type InProceedings.
- 711 label "Recommending Questions Using the MDL-based Tree Cut Model".
- 711 sameAs 711.
- 711 abstract "The paper is concerned with the problem of question recommendation. Specifically, given a question as query, we are to retrieve and rank other questions according to their likelihood of being good recommendations of the queried question. A good recommendation provides alternative aspects around users’ interest. We tackle the problem of question recommendation in two steps: first represent questions as graphs of topic terms, and then rank recommendations on the basis of the graphs. We formalize both steps as the tree-cutting problems and then employ the MDL (Minimum Description Length) for selecting the best cuts. Experiments have been conducted with the real questions posted at Yahoo! Answers. The questions are about two domains, ‘travel’ and ‘computers & internet’. Experimental results indicate that the use of the MDL-based tree cut model can significantly outperform the baseline methods of word-based VSM or phrase-based VSM. The results also show that the use of the MDL-based tree cut model is essential to our approach.".
- 711 hasAuthorList authorList.
- 711 hasTopic World_Wide_Web.
- 711 isPartOf proceedings.
- 711 keyword "minimum description length".
- 711 keyword "query suggestion".
- 711 keyword "question recommendation".
- 711 keyword "tree cut model".
- 711 title "Recommending Questions Using the MDL-based Tree Cut Model".
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- item-1 hasContent yunbo-cao.
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- item-2 hasContent huizhong-duan.
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- item-3 hasContent chin-yew-lin.
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- 729 creator hamid-nazerzadeh.
- 729 creator nicole-immorlica.
- 729 creator uriel-feige.
- 729 creator vahab-mirrokni.
- 729 type InProceedings.
- 729 label "A combinatorial allocation mechanism with penalties for banner advertising".
- 729 sameAs 729.
- 729 abstract "Most current banner advertising is sold through negotiation thereby incurring large transaction costs and possibly suboptimal allocations. We propose a new automated system for selling banner advertising. In this system, each advertiser specifies a collection of host webpages which are relevant to his product, a desired total quantity of impressions on these pages, and a maximum per-impression price. The system selects a subset of advertisers as {\em winners} and maps each winner to a set of impressions on pages within his desired collection. The distinguishing feature of our system as opposed to current combinatorial allocation mechanisms is that, mimicking the current negotiation system, we guarantee that winners receive at least as many advertising opportunities as they requested or else receive ample compensation in the form of monetary paid by the host. Such guarantees are essential in markets like banner advertising where a major goal of the advertising campaign is developing brand recognition. As we show, the problem of selecting a feasible subset of advertisers with maximum total value is inapproximable. We thus present two greedy heuristic and discuss theoretical techniques to measures their performances. Our first algorithm iteratively selects advertisers and corresponding sets of impressions which contribute maximum marginal per-impression profit to the current solution. We prove a {\em bi-criteria approximation} for this algorithm, showing that it generates approximately as much value as the optimum algorithm on a slightly harder problem. However, this algorithm may perform poorly on instances in which the value of the optimum solution is quite large, a clearly undesirable failure mode. Hence, we present an adaptive greedy algorithm which again iteratively selects advertisers with maximum marginal per-impression profit, but additionally reassigns impressions at each iteration. For this algorithm, we prove a {\em functional approximation} result, a newly defined framework for evaluating heuristics~\cite{FIMN08}. We thereby prove that this algorithm has a better performance guarantee than the simple greedy algorithm when the optimum value is large.".
- 729 hasAuthorList authorList.
- 729 hasTopic World_Wide_Web.
- 729 isPartOf proceedings.
- 729 keyword "Approximation Algorithm".
- 729 keyword "Combinatorial Auction".
- 729 keyword "Functional Approximation".
- 729 keyword "Internet Advertising".
- 729 keyword "Supply Guarantee".
- 729 title "A combinatorial allocation mechanism with penalties for banner advertising".
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- item-1 hasContent uriel-feige.
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- item-2 hasContent nicole-immorlica.
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- item-3 hasContent vahab-mirrokni.
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- item-4 hasContent hamid-nazerzadeh.
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- 736 creator deepak-agarwal.
- 736 creator deepayan-chakrabarti.
- 736 creator vanja-josifovski.
- 736 type InProceedings.
- 736 label "Contextual Advertising by Combining Relevance with Click Feedback".
- 736 sameAs 736.
- 736 abstract "Contextual advertising supports much of the Web's ecosystem today. User experience and revenue (shared by the site publisher ad the ad network) depend on the relevance of the displayed ads to the page content. As with other document retrieval systems, relevance is provided by scoring the match between individual ads (documents) and the content of the page where the ads are shown (query). In this paper we show how this match can be improved significantly by augmenting the ad-page scoring function with extra parameters from a logistic regression model on the words in the pages and ads. A key property of the proposed model is that it can be mapped to standard cosine similarity matching and is suitable for efficient and scalable implementation over inverted indexes. The model parameter values are learnt from logs containing ad impressions and clicks, with shrinkage estimators being used to combat sparsity. To scale our computations to train on an extremely large training corpus consisting of several gigabytes of data, we parallelize our fitting algorithm in a Hadoop framework. Experimental evaluation is provided showing improved click prediction over a holdout set of impression and click events from a large scale real-world ad placement engine. Our best model achieves a 25% lift in precision relative to a traditional information retrieval model which is based on cosine similarity, for recalling 10% of the clicks in our test data.".
- 736 hasAuthorList authorList.
- 736 hasTopic World_Wide_Web.
- 736 isPartOf proceedings.
- 736 keyword "Clickthrough rate".
- 736 keyword "Interaction effects".
- 736 keyword "Modeling".
- 736 title "Contextual Advertising by Combining Relevance with Click Feedback".
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- item-1 hasContent deepayan-chakrabarti.
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- item-2 hasContent deepak-agarwal.
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