NDLI: (big) usage data in web search

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LaFT-tree: perceiving the expansion trace of one's circle of friends in online social networks

Learning to rank for spatiotemporal search

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Models and algorithms for social influence analysis

Cascade-based community detection

Identifying users' topical tasks in web search

Arrival and departure dynamics in social networks

Personalizing atypical web search sessions

Wiki3C: exploiting wikipedia for context-aware concept categorization

Translating related words to videos and back through latent topics

Connecting comments and tags: improved modeling of social tagging systems

A peek into the future: predicting the evolution of popularity in user generated content

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Optimizing top-k document retrieval strategies for block-max indexes

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Geo topic model: joint modeling of user's activity area and interests for location recommendation

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(big) usage data in web search

Playing by the rules: mining query associations to predict search performance

Latent factor models with additive and hierarchically-smoothed user preferences

Collective inference for network data with copula latent markov networks

Econometric analysis and digital marketing: how to measure the effectiveness of an ad

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WSCD2013: workshop on web search click data 2013

Rank quantization

Threading machine generated email

Modeling dynamic behavior in large evolving graphs

3rd workshop on context-awareness in retrieval and recommendation

Time-sensitive web image ranking and retrieval via dynamic multi-task regression

Predicting content change on the web

On the streaming complexity of computing local clustering coefficients

Workshop on large-scale and distributed systems for information retrieval (LSDS-IR 2013)

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Learning query and document similarities from click-through bipartite graph with metadata

Workshop on semantic personalized information management (SPIM'13)

Reusing historical interaction data for faster online learning to rank for IR

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Search and exploration of X-Rated information (SEXI 2013)

Pairwise ranking aggregation in a crowdsourced setting

Learning multiple-question decision trees for cold-start recommendation

(big) usage data in web search

Content Provider	ACM Digital Library
Author	Baeza-Yates, Ricardo Maarek, Yoelle
Abstract	Web Search, which takes its root in the mature field of information retrieval, evolved tremendously over the last 15 years. The field encountered its first revolution when it started to deal with huge amounts of Web pages. Then, a major step was accomplished when engines started to consider the structure of the Web graph and leveraged link analysis in both crawling and ranking. Finally, a more discrete, but no less critical step, was made when search engines started to monitor and exploit the numerous (mostly implicit) signals provided by users while interacting with the search engine. In this tutorial we focus on this "revolution" of large scale usage data. In the first part of this tutorial, we focus on usage data, which typically refers to any type of information provided by the user while interacting with the search engine. It comes first under its raw form as a set of individual signals, but is typically mined after multiple signals have been aggregated and linked to the same interaction event. The two major types of such data are (1) query streams, which include the query string that the user issued, together with the time-stamp of the query, a user identifier, possibly the IP of the machine on which the browser runs, and (2) click data, which include the reference to the element the user clicked on the page together with the timestamp, user identifier, possibly IP, the rank of the link if it is a result, etc. Exploiting usage data under its multiple forms brought an unprecedented wealth of implicit information to Web Search. We discuss in the second part of this tutorial some of the key Web search applications that it made possible. One such example is the query spelling correction feature embodied now in all search engines. In fact, after years of very sophisticated spell checking research, simply counting similar queries at a small edit distance would in most cases surface the most popular spelling as the correct one, a beautiful and simple demonstration of the wisdom of crowds principle.
Starting Page	783
Ending Page	784
Page Count	2
File Format	PDF
ISBN	9781450318693
DOI	10.1145/2433396.2433501
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-02-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Web retrieval User interaction
Content Type	Text
Resource Type	Article

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