12,459 research outputs found
Unbiased Comparative Evaluation of Ranking Functions
Eliciting relevance judgments for ranking evaluation is labor-intensive and
costly, motivating careful selection of which documents to judge. Unlike
traditional approaches that make this selection deterministically,
probabilistic sampling has shown intriguing promise since it enables the design
of estimators that are provably unbiased even when reusing data with missing
judgments. In this paper, we first unify and extend these sampling approaches
by viewing the evaluation problem as a Monte Carlo estimation task that applies
to a large number of common IR metrics. Drawing on the theoretical clarity that
this view offers, we tackle three practical evaluation scenarios: comparing two
systems, comparing systems against a baseline, and ranking systems. For
each scenario, we derive an estimator and a variance-optimizing sampling
distribution while retaining the strengths of sampling-based evaluation,
including unbiasedness, reusability despite missing data, and ease of use in
practice. In addition to the theoretical contribution, we empirically evaluate
our methods against previously used sampling heuristics and find that they
generally cut the number of required relevance judgments at least in half.Comment: Under review; 10 page
Search Bias Quantification: Investigating Political Bias in Social Media and Web Search
Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives. Given the considerable impact that search bias can have on the user, we propose a generalizable search bias quantification framework that not only measures the political bias in ranked list output by the search system but also decouples the bias introduced by the different sources—input data and ranking system. We apply our framework to study the political bias in searches related to 2016 US Presidential primaries in Twitter social media search and find that both input data and ranking system matter in determining the final search output bias seen by the users. And finally, we use the framework to compare the relative bias for two popular search systems—Twitter social media search and Google web search—for queries related to politicians and political events. We end by discussing some potential solutions to signal the bias in the search results to make the users more aware of them.publishe
A Vertical PRF Architecture for Microblog Search
In microblog retrieval, query expansion can be essential to obtain good
search results due to the short size of queries and posts. Since information in
microblogs is highly dynamic, an up-to-date index coupled with pseudo-relevance
feedback (PRF) with an external corpus has a higher chance of retrieving more
relevant documents and improving ranking. In this paper, we focus on the
research question:how can we reduce the query expansion computational cost
while maintaining the same retrieval precision as standard PRF? Therefore, we
propose to accelerate the query expansion step of pseudo-relevance feedback.
The hypothesis is that using an expansion corpus organized into verticals for
expanding the query, will lead to a more efficient query expansion process and
improved retrieval effectiveness. Thus, the proposed query expansion method
uses a distributed search architecture and resource selection algorithms to
provide an efficient query expansion process. Experiments on the TREC Microblog
datasets show that the proposed approach can match or outperform standard PRF
in MAP and NDCG@30, with a computational cost that is three orders of magnitude
lower.Comment: To appear in ICTIR 201
Evaluating the retrieval effectiveness of Web search engines using a representative query sample
Search engine retrieval effectiveness studies are usually small-scale, using
only limited query samples. Furthermore, queries are selected by the
researchers. We address these issues by taking a random representative sample
of 1,000 informational and 1,000 navigational queries from a major German
search engine and comparing Google's and Bing's results based on this sample.
Jurors were found through crowdsourcing, data was collected using specialised
software, the Relevance Assessment Tool (RAT). We found that while Google
outperforms Bing in both query types, the difference in the performance for
informational queries was rather low. However, for navigational queries, Google
found the correct answer in 95.3 per cent of cases whereas Bing only found the
correct answer 76.6 per cent of the time. We conclude that search engine
performance on navigational queries is of great importance, as users in this
case can clearly identify queries that have returned correct results. So,
performance on this query type may contribute to explaining user satisfaction
with search engines
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval
In Online Learning to Rank (OLTR) the aim is to find an optimal ranking model
by interacting with users. When learning from user behavior, systems must
interact with users while simultaneously learning from those interactions.
Unlike other Learning to Rank (LTR) settings, existing research in this field
has been limited to linear models. This is due to the speed-quality tradeoff
that arises when selecting models: complex models are more expressive and can
find the best rankings but need more user interactions to do so, a requirement
that risks frustrating users during training. Conversely, simpler models can be
optimized on fewer interactions and thus provide a better user experience, but
they will converge towards suboptimal rankings. This tradeoff creates a
deadlock, since novel models will not be able to improve either the user
experience or the final convergence point, without sacrificing the other. Our
contribution is twofold. First, we introduce a fast OLTR model called Sim-MGD
that addresses the speed aspect of the speed-quality tradeoff. Sim-MGD ranks
documents based on similarities with reference documents. It converges rapidly
and, hence, gives a better user experience but it does not converge towards the
optimal rankings. Second, we contribute Cascading Multileave Gradient Descent
(C-MGD) for OLTR that directly addresses the speed-quality tradeoff by using a
cascade that enables combinations of the best of two worlds: fast learning and
high quality final convergence. C-MGD can provide the better user experience of
Sim-MGD while maintaining the same convergence as the state-of-the-art MGD
model. This opens the door for future work to design new models for OLTR
without having to deal with the speed-quality tradeoff.Comment: CIKM 2017, Proceedings of the 2017 ACM on Conference on Information
and Knowledge Managemen
Active Sampling for Large-scale Information Retrieval Evaluation
Evaluation is crucial in Information Retrieval. The development of models,
tools and methods has significantly benefited from the availability of reusable
test collections formed through a standardized and thoroughly tested
methodology, known as the Cranfield paradigm. Constructing these collections
requires obtaining relevance judgments for a pool of documents, retrieved by
systems participating in an evaluation task; thus involves immense human labor.
To alleviate this effort different methods for constructing collections have
been proposed in the literature, falling under two broad categories: (a)
sampling, and (b) active selection of documents. The former devises a smart
sampling strategy by choosing only a subset of documents to be assessed and
inferring evaluation measure on the basis of the obtained sample; the sampling
distribution is being fixed at the beginning of the process. The latter
recognizes that systems contributing documents to be judged vary in quality,
and actively selects documents from good systems. The quality of systems is
measured every time a new document is being judged. In this paper we seek to
solve the problem of large-scale retrieval evaluation combining the two
approaches. We devise an active sampling method that avoids the bias of the
active selection methods towards good systems, and at the same time reduces the
variance of the current sampling approaches by placing a distribution over
systems, which varies as judgments become available. We validate the proposed
method using TREC data and demonstrate the advantages of this new method
compared to past approaches
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