18,899 research outputs found
Query Chains: Learning to Rank from Implicit Feedback
This paper presents a novel approach for using clickthrough data to learn
ranked retrieval functions for web search results. We observe that users
searching the web often perform a sequence, or chain, of queries with a similar
information need. Using query chains, we generate new types of preference
judgments from search engine logs, thus taking advantage of user intelligence
in reformulating queries. To validate our method we perform a controlled user
study comparing generated preference judgments to explicit relevance judgments.
We also implemented a real-world search engine to test our approach, using a
modified ranking SVM to learn an improved ranking function from preference
data. Our results demonstrate significant improvements in the ranking given by
the search engine. The learned rankings outperform both a static ranking
function, as well as one trained without considering query chains.Comment: 10 page
Target Type Identification for Entity-Bearing Queries
Identifying the target types of entity-bearing queries can help improve
retrieval performance as well as the overall search experience. In this work,
we address the problem of automatically detecting the target types of a query
with respect to a type taxonomy. We propose a supervised learning approach with
a rich variety of features. Using a purpose-built test collection, we show that
our approach outperforms existing methods by a remarkable margin. This is an
extended version of the article published with the same title in the
Proceedings of SIGIR'17.Comment: Extended version of SIGIR'17 short paper, 5 page
Porqpine: a peer-to-peer search engine
In this paper, we present a fully distributed and collaborative search
engine for web pages: Porqpine. This system uses a novel query-based model
and collaborative filtering techniques in order to obtain user-customized
results. All knowledge about users and profiles is stored in each user
node?s application. Overall the system is a multi-agent system that runs on
the computers of the user community. The nodes interact in a peer-to-peer
fashion in order to create a real distributed search engine where
information is completely distributed among all the nodes in the network.
Moreover, the system preserves the privacy of user queries and results by
maintaining the anonymity of the queries? consumers and results? producers.
The knowledge required by the system to work is implicitly caught through
the monitoring of users actions, not only within the system?s interface but
also within one of the most popular web browsers. Thus, users are not
required to explicitly feed knowledge about their interests into the system
since this process is done automatically. In this manner, users obtain the
benefits of a personalized search engine just by installing the application
on their computer. Porqpine does not intend to shun completely conventional
centralized search engines but to complement them by issuing more accurate
and personalized results.Postprint (published version
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