Click-through data has been used in various ways in Web search such as estimating relevance between documents and queries. Since only search snippets are perceived by users before issuing any clicks, the relevance induced by clicks are usually called which has proven to be quite useful for Web search. While there is plenty of click data for popular queries, very little information is available for unpopular tail ones. These tail queries take a large portion of the search volume but search accuracy for these queries is usually unsatisfactory due to data sparseness such as limited click information. In this paper, we study the problem of modeling perceived relevance for queries without click-through data. Instead of relying on users' click data, we carefully design a set of snippet features and use them to approximately capture the perceived relevance. We study the effectiveness of this set of snippet features in two settings: (1) predicting perceived relevance and (2) enhancing search engine ranking. Experimental results show that our proposed model is effective to predict the relative perceived relevance of Web search results. Furthermore, our proposed snippet features are effective to improve search accuracy for longer tail queries without click-through data.
Our work is also related to click prediction works @cite_36 @cite_26 @cite_17 . @cite_36 used an existing hierarchy to propagate clicks to rare events. @cite_17 used past clicks to predict future. @cite_26 proposed a feature-based method of predicting the click-through rate for new ads. To the best of our knowledge, few works have been conducted to predict click-based perceived relevance for tail queries in Web search. Furthermore, compared with @cite_26 which only uses query-dependent features, we explore a more compressive feature set with both query-dependent and query-independent features.
@cite_36 @cite_26 @cite_17
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We consider the problem of estimating occurrence rates of rare eventsfor extremely sparse data, using pre-existing hierarchies to perform inference at multiple resolutions. In particular, we focus on the problem of estimating click rates for (webpage, advertisement) pairs (called impressions) where both the pages and the ads are classified into hierarchies that capture broad contextual information at different levels of granularity. Typically the click rates are low and the coverage of the hierarchies is sparse. To overcome these difficulties we devise a sampling method whereby we analyze aspecially chosen sample of pages in the training set, and then estimate click rates using a two-stage model. The first stage imputes the number of (webpage, ad) pairs at all resolutions of the hierarchy to adjust for the sampling bias. The second stage estimates clickrates at all resolutions after incorporating correlations among sibling nodes through a tree-structured Markov model. Both models are scalable and suited to large scale data mining applications. On a real-world dataset consisting of 1 2 billion impressions, we demonstrate that even with 95 negative (non-clicked) events in the training set, our method can effectively discriminate extremely rare events in terms of their click propensity. Search engine advertising has become a significant element of the Web browsing experience. Choosing the right ads for the query and the order in which they are displayed greatly affects the probability that a user will see and click on each ad. This ranking has a strong impact on the revenue the search engine receives from the ads. Further, showing the user an ad that they prefer to click on improves user satisfaction. For these reasons, it is important to be able to accurately estimate the click-through rate of ads in the system. For ads that have been displayed repeatedly, this is empirically measurable, but for new ads, other means must be used. We show that we can use features of ads, terms, and advertisers to learn a model that accurately predicts the click-though rate for new ads. We also show that using our model improves the convergence and performance of an advertising system. As a result, our model increases both revenue and user satisfaction. Search engine click logs provide an invaluable source of relevance information but this information is biased because we ignore which documents from the result list the users have actually seen before and after they clicked. Otherwise, we could estimate document relevance by simple counting. In this paper, we propose a set of assumptions on user browsing behavior that allows the estimation of the probability that a document is seen, thereby providing an unbiased estimate of document relevance. To train, test and compare our model to the best alternatives described in the Literature, we gather a large set of real data and proceed to an extensive cross-validation experiment. Our solution outperforms very significantly all previous models. As a side effect, we gain insight into the browsing behavior of users and we can compare it to the conclusions of an eye-tracking experiments by [12]. In particular, our findings confirm that a user almost always see the document directly after a clicked document. They also explain why documents situated just after a very relevant document are clicked more often.