19,522 research outputs found
A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion
Users may strive to formulate an adequate textual query for their information
need. Search engines assist the users by presenting query suggestions. To
preserve the original search intent, suggestions should be context-aware and
account for the previous queries issued by the user. Achieving context
awareness is challenging due to data sparsity. We present a probabilistic
suggestion model that is able to account for sequences of previous queries of
arbitrary lengths. Our novel hierarchical recurrent encoder-decoder
architecture allows the model to be sensitive to the order of queries in the
context while avoiding data sparsity. Additionally, our model can suggest for
rare, or long-tail, queries. The produced suggestions are synthetic and are
sampled one word at a time, using computationally cheap decoding techniques.
This is in contrast to current synthetic suggestion models relying upon machine
learning pipelines and hand-engineered feature sets. Results show that it
outperforms existing context-aware approaches in a next query prediction
setting. In addition to query suggestion, our model is general enough to be
used in a variety of other applications.Comment: To appear in Conference of Information Knowledge and Management
(CIKM) 201
IEAD: A Novel One-Line Interface to Query Astronomical Science Archives
In this article I present IEAD, a new interface for astronomical science
databases. It is based on a powerful, yet simple, syntax designed to completely
abstract the user from the structure of the underlying database. The
programming language chosen for its implementation, JavaScript, makes it
possible to interact directly with the user and to provide real-time
information on the parsing process, error messages, and the name resolution of
targets; additionally, the same parsing engine is used for context-sensitive
autocompletion. Ultimately, this product should significantly simplify the use
of astronomical archives, inspire more advanced uses of them, and allow the
user to focus on what scientific research to perform, instead of on how to
instruct the computer to do it.Comment: 13 pages, PASP in pres
Intent Models for Contextualising and Diversifying Query Suggestions
The query suggestion or auto-completion mechanisms help users to type less
while interacting with a search engine. A basic approach that ranks suggestions
according to their frequency in the query logs is suboptimal. Firstly, many
candidate queries with the same prefix can be removed as redundant. Secondly,
the suggestions can also be personalised based on the user's context. These two
directions to improve the aforementioned mechanisms' quality can be in
opposition: while the latter aims to promote suggestions that address search
intents that a user is likely to have, the former aims to diversify the
suggestions to cover as many intents as possible. We introduce a
contextualisation framework that utilises a short-term context using the user's
behaviour within the current search session, such as the previous query, the
documents examined, and the candidate query suggestions that the user has
discarded. This short-term context is used to contextualise and diversify the
ranking of query suggestions, by modelling the user's information need as a
mixture of intent-specific user models. The evaluation is performed offline on
a set of approximately 1.0M test user sessions. Our results suggest that the
proposed approach significantly improves query suggestions compared to the
baseline approach.Comment: A short version of this paper was presented at CIKM 201
Efficient and Effective Query Auto-Completion
Query Auto-Completion (QAC) is an ubiquitous feature of modern textual search
systems, suggesting possible ways of completing the query being typed by the
user. Efficiency is crucial to make the system have a real-time responsiveness
when operating in the million-scale search space. Prior work has extensively
advocated the use of a trie data structure for fast prefix-search operations in
compact space. However, searching by prefix has little discovery power in that
only completions that are prefixed by the query are returned. This may impact
negatively the effectiveness of the QAC system, with a consequent monetary loss
for real applications like Web Search Engines and eCommerce. In this work we
describe the implementation that empowers a new QAC system at eBay, and discuss
its efficiency/effectiveness in relation to other approaches at the
state-of-the-art. The solution is based on the combination of an inverted index
with succinct data structures, a much less explored direction in the
literature. This system is replacing the previous implementation based on
Apache SOLR that was not always able to meet the required
service-level-agreement.Comment: Published in SIGIR 202
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