1,938 research outputs found
On Suggesting Entities as Web Search Queries
The Web of Data is growing in popularity and dimension,
and named entity exploitation is gaining importance in many research
fields. In this paper, we explore the use of entities that can be extracted
from a query log to enhance query recommendation. In particular, we
extend a state-of-the-art recommendation algorithm to take into account
the semantic information associated with submitted queries. Our novel
method generates highly related and diversified suggestions that we as-
sess by means of a new evaluation technique. The manually annotated
dataset used for performance comparisons has been made available to
the research community to favor the repeatability of experiments
Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising
Sponsored search represents a major source of revenue for web search engines.
This popular advertising model brings a unique possibility for advertisers to
target users' immediate intent communicated through a search query, usually by
displaying their ads alongside organic search results for queries deemed
relevant to their products or services. However, due to a large number of
unique queries it is challenging for advertisers to identify all such relevant
queries. For this reason search engines often provide a service of advanced
matching, which automatically finds additional relevant queries for advertisers
to bid on. We present a novel advanced matching approach based on the idea of
semantic embeddings of queries and ads. The embeddings were learned using a
large data set of user search sessions, consisting of search queries, clicked
ads and search links, while utilizing contextual information such as dwell time
and skipped ads. To address the large-scale nature of our problem, both in
terms of data and vocabulary size, we propose a novel distributed algorithm for
training of the embeddings. Finally, we present an approach for overcoming a
cold-start problem associated with new ads and queries. We report results of
editorial evaluation and online tests on actual search traffic. The results
show that our approach significantly outperforms baselines in terms of
relevance, coverage, and incremental revenue. Lastly, we open-source learned
query embeddings to be used by researchers in computational advertising and
related fields.Comment: 10 pages, 4 figures, 39th International ACM SIGIR Conference on
Research and Development in Information Retrieval, SIGIR 2016, Pisa, Ital
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
Query Log Mining to Enhance User Experience in Search Engines
The Web is the biggest repository of documents humans have ever built. Even more, it is increasingly growing in size every day. Users rely on Web search engines (WSEs) for finding information on the Web. By submitting a textual query expressing their information need, WSE users obtain a list of documents that are highly relevant to the query. Moreover, WSEs tend to store such huge amount of users activities in "query logs". Query log mining is the set of techniques aiming at extracting valuable knowledge from query logs. This knowledge represents one of the most used ways of enhancing the users’ search experience. According to this vision, in this thesis we firstly prove that the knowledge extracted from query logs suffer aging effects and we thus propose a solution to this phenomenon. Secondly, we propose new algorithms for query recommendation that overcome the aging problem. Moreover, we study new query recommendation techniques for efficiently producing recommendations for rare queries. Finally, we study the problem of diversifying Web search engine results. We define a methodology based on the knowledge derived from query logs for detecting when and how query results need to be diversified and we develop an efficient algorithm for diversifying search results
Efficient query processing for scalable web search
Search engines are exceptionally important tools for accessing information in today’s world. In satisfying the information needs of millions of users, the effectiveness (the quality of the search results) and the efficiency (the speed at which the results are returned to the users) of a search engine are two goals that form a natural trade-off, as techniques that improve the effectiveness of the search engine can also make it less efficient. Meanwhile, search engines continue to rapidly evolve, with larger indexes, more complex retrieval strategies and growing query volumes. Hence, there is a need for the development of efficient query processing infrastructures that make appropriate sacrifices in effectiveness in order to make gains in efficiency. This survey comprehensively reviews the foundations of search engines, from index layouts to basic term-at-a-time (TAAT) and document-at-a-time (DAAT) query processing strategies, while also providing the latest trends in the literature in efficient query processing, including the coherent and systematic reviews of techniques such as dynamic pruning and impact-sorted posting lists as well as their variants and optimisations. Our explanations of query processing strategies, for instance the WAND and BMW dynamic pruning algorithms, are presented with illustrative figures showing how the processing state changes as the algorithms progress. Moreover, acknowledging the recent trends in applying a cascading infrastructure within search systems, this survey describes techniques for efficiently integrating effective learned models, such as those obtained from learning-to-rank techniques. The survey also covers the selective application of query processing techniques, often achieved by predicting the response times of the search engine (known as query efficiency prediction), and making per-query tradeoffs between efficiency and effectiveness to ensure that the required retrieval speed targets can be met. Finally, the survey concludes with a summary of open directions in efficient search infrastructures, namely the use of signatures, real-time, energy-efficient and modern hardware and software architectures
Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval
Neural networks with deep architectures have demonstrated significant
performance improvements in computer vision, speech recognition, and natural
language processing. The challenges in information retrieval (IR), however, are
different from these other application areas. A common form of IR involves
ranking of documents--or short passages--in response to keyword-based queries.
Effective IR systems must deal with query-document vocabulary mismatch problem,
by modeling relationships between different query and document terms and how
they indicate relevance. Models should also consider lexical matches when the
query contains rare terms--such as a person's name or a product model
number--not seen during training, and to avoid retrieving semantically related
but irrelevant results. In many real-life IR tasks, the retrieval involves
extremely large collections--such as the document index of a commercial Web
search engine--containing billions of documents. Efficient IR methods should
take advantage of specialized IR data structures, such as inverted index, to
efficiently retrieve from large collections. Given an information need, the IR
system also mediates how much exposure an information artifact receives by
deciding whether it should be displayed, and where it should be positioned,
among other results. Exposure-aware IR systems may optimize for additional
objectives, besides relevance, such as parity of exposure for retrieved items
and content publishers. In this thesis, we present novel neural architectures
and methods motivated by the specific needs and challenges of IR tasks.Comment: PhD thesis, Univ College London (2020
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