Search engine logs contain a large amount of users' click-through data that can be leveraged as implicit indicators of relevance. In this paper we address ad recommendation problem that finding and ranking the most relevant ads with respect to users' ...

Similarity search is one of the fundamental problems for large scale multimedia applications. Hashing techniques, as one popular strategy, have been intensively investigated owing to the speed and memory efficiency. Recent research has shown that ...

One of the fundamental problems in image search is to rank image documents according to a given textual query. We address two limitations of the existing image search engines in this paper. First, there is no straightforward way of comparing textual ...

Image search engines have achieved good performance for head (popular) queries by leveraging text information and user click data. However, there still remain a large number of tail (rare) queries with relatively unsatisfying search results, which are ...

One of the fundamental problems in image search is to rank image documents according to a given textual query. Existing search engines highly depend on surrounding texts for ranking images, or leverage the query-image pairs annotated by human labelers ...

Our objective is to estimate the relevance of an image to a query for image search purposes. We address two limitations of the existing image search engines in this paper. First, there is no straightforward way of bridging the gap between semantic ...

The problem of tagging is mostly considered from the perspectives of machine learning and data-driven philosophy. A fundamental issue that underlies the success of these approaches is the visual similarity, ranging from the nearest neighbor search to ...

Query suggestion helps users to precisely express their search intents. The state-of-the-art approaches make great progress on high-frequency queries via click graphs. However, due to query ambiguity and click-through data sparseness, these approaches ...

An important way to analyze the web click-through data is to build up a 2-class linear classifier, and select a key subset from user's features which mainly decided the hit result. But in many circumstances, the fitting accuracy is not good as the model ...

Query suggestion plays an important role in improving usability of search engines. Although some recently proposed methods provide query suggestions by mining query patterns from search logs, none of them models the immediately preceding queries as ...

This paper addresses the problem of dealing with term mismatch in web search using 'blending'. In blending, the input query as well as queries similar to it are used to retrieve documents, the ranking results of documents with respect to the queries are ...

Document classification plays an increasingly important role in extracting and organizing the knowledge, however, the Web document classification task was hindered by the huge number of Web documents while limited resource of human judgment on the ...

Most information retrieval evaluation metrics are designed to measure the satisfaction of the user given the results returned by a search engine. In order to evaluate user satisfaction, most of these metrics have underlying user models, which aim at ...

We explore the potential of using users click-through logs where no editorial judgment is available to improve the ranking function of a vertical search engine. We base our analysis on the Cumulate Relevance Model, a user behavior model recently ...

Interleaving experiments are an attractive methodology for evaluating retrieval functions through implicit feedback. Designed as a blind and unbiased test for eliciting a preference between two retrieval functions, an interleaved ranking of the results ...

Detecting events from web resources is a challenging task, attracting many attentions in recent years. Web search log is an important data source for event detection because the information it contains reflects users' activities and interestingness to ...

Mining feedback information from user click-through data is an important issue for modern Web retrieval systems in terms of architecture analysis, performance evaluation and algorithm optimization. For commercial search engines, user click-through data ...

In this paper, we present a semi-supervised learning method for web page classification, leveraging click logs to augment training data by propagating class labels to unlabeled similar documents. Current state-of-the-art classifiers are supervised and ...

To overcome the training data insufficiency problem for dedicated model in topical ranking, this paper proposes to utilize click-through data to improve learning. The efficacy of click-through data is explored under the framework of preference learning. ...

Both Contextual Advertising (CA) and Behavioral Targeting (BT) are playing important roles in online advertising market. Recently, the problem of how to integrate BT strategies into CA has attracted much attention from both industry and academia. ...