361 research outputs found
Combining Text and Formula Queries in Math Information Retrieval: Evaluation of Query Results Merging Strategies
Specific to Math Information Retrieval is combining text with mathematical
formulae both in documents and in queries. Rigorous evaluation of query
expansion and merging strategies combining math and standard textual keyword
terms in a query are given. It is shown that techniques similar to those known
from textual query processing may be applied in math information retrieval as
well, and lead to a cutting edge performance. Striping and merging partial
results from subqueries is one technique that improves results measured by
information retrieval evaluation metrics like Bpref
Sensitive and Scalable Online Evaluation with Theoretical Guarantees
Multileaved comparison methods generalize interleaved comparison methods to
provide a scalable approach for comparing ranking systems based on regular user
interactions. Such methods enable the increasingly rapid research and
development of search engines. However, existing multileaved comparison methods
that provide reliable outcomes do so by degrading the user experience during
evaluation. Conversely, current multileaved comparison methods that maintain
the user experience cannot guarantee correctness. Our contribution is two-fold.
First, we propose a theoretical framework for systematically comparing
multileaved comparison methods using the notions of considerateness, which
concerns maintaining the user experience, and fidelity, which concerns reliable
correct outcomes. Second, we introduce a novel multileaved comparison method,
Pairwise Preference Multileaving (PPM), that performs comparisons based on
document-pair preferences, and prove that it is considerate and has fidelity.
We show empirically that, compared to previous multileaved comparison methods,
PPM is more sensitive to user preferences and scalable with the number of
rankers being compared.Comment: CIKM 2017, Proceedings of the 2017 ACM on Conference on Information
and Knowledge Managemen
Synchronous collaborative information retrieval: techniques and evaluation
Synchronous Collaborative Information Retrieval refers to
systems that support multiple users searching together at the same time in order to satisfy a shared information need. To date most SCIR systems have focussed on providing various awareness tools in order to enable collaborating users to coordinate the search task. However, requiring users to both search and coordinate the group activity may prove too demanding. On the other hand without effective coordination policies the group search may not be effective. In this paper we propose and evaluate novel system-mediated techniques for coordinating a group search. These techniques allow for an effective division of labour across the group whereby each group member can explore a subset of the search space.We also propose and evaluate techniques to support automated sharing of knowledge across searchers in SCIR, through novel collaborative and complementary relevance feedback techniques. In order to evaluate these techniques, we propose a framework for SCIR evaluation based on simulations. To populate these simulations we extract data from TREC interactive search logs. This work represent the first simulations of SCIR to date and the first such use of this TREC data
Use of implicit graph for recommending relevant videos: a simulated evaluation
In this paper, we propose a model for exploiting community based usage information for video retrieval. Implicit usage information from a pool of past users could be a valuable source to address the difficulties caused due to the semantic gap problem. We propose a graph-based implicit feedback model in which all the usage information can be represented. A number of recommendation algorithms were suggested and experimented. A simulated user evaluation is conducted on the TREC VID collection and the results are presented. Analyzing the results we found some common characteristics on the best performing algorithms, which could indicate the best way of exploiting this type of usage information
Unsupervised, Efficient and Semantic Expertise Retrieval
We introduce an unsupervised discriminative model for the task of retrieving
experts in online document collections. We exclusively employ textual evidence
and avoid explicit feature engineering by learning distributed word
representations in an unsupervised way. We compare our model to
state-of-the-art unsupervised statistical vector space and probabilistic
generative approaches. Our proposed log-linear model achieves the retrieval
performance levels of state-of-the-art document-centric methods with the low
inference cost of so-called profile-centric approaches. It yields a
statistically significant improved ranking over vector space and generative
models in most cases, matching the performance of supervised methods on various
benchmarks. That is, by using solely text we can do as well as methods that
work with external evidence and/or relevance feedback. A contrastive analysis
of rankings produced by discriminative and generative approaches shows that
they have complementary strengths due to the ability of the unsupervised
discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World
Wide Web. 201
Study of Relevance and Effort across Devices
Relevance judgements are essential for designing information
retrieval systems. Traditionally, judgements have been judgements have been gathered via desktop interfaces. However, with the rise in popularity of smaller devices for information access, it has become imperative to investigate whether desktop based judgements are different from judgements gathered using mobiles. Recently, user effort and document usefulness have also emerged as important dimensions to optimize and evaluate information retrieval systems. Since existing work is limited to desktops, it remains to be seen how these judgements are affected by user’s search device.
In this paper, we address these shortcomings by collecting
and analyzing relevance, usefulness and effort judgements on
mobiles and desktops. Analysis of these judgements indicates
that high agreement rate between desktop and mobile judges
for relevance, followed by usefulness and findability. We also found that desktop judges are likely to spend more time and examine documents in greater depth on non-relevant/notuseful/difficult documents compared to mobile judges. Based on our findings, we suggest that relevance judgements should be gathered via desktops and effort judgements should be collected on each device independently
Towards Spatial Word Embeddings
Leveraging textual and spatial data provided in spatio-textual objects (eg., tweets), has become increasingly important in real-world applications, favoured by the increasing rate of their availability these last decades (eg., through smartphones). In this paper, we propose a spatial retrofitting method of word embeddings that could reveal the localised similarity of word pairs as well as the diversity of their localised meanings. Experiments based on the semantic location prediction task show that our method achieves significant improvement over strong baselines
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