24,406 research outputs found
From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach
One key challenge in talent search is to translate complex criteria of a
hiring position into a search query, while it is relatively easy for a searcher
to list examples of suitable candidates for a given position. To improve search
efficiency, we propose the next generation of talent search at LinkedIn, also
referred to as Search By Ideal Candidates. In this system, a searcher provides
one or several ideal candidates as the input to hire for a given position. The
system then generates a query based on the ideal candidates and uses it to
retrieve and rank results. Shifting from the traditional Query-By-Keyword to
this new Query-By-Example system poses a number of challenges: How to generate
a query that best describes the candidates? When moving to a completely
different paradigm, how does one leverage previous product logs to learn
ranking models and/or evaluate the new system with no existing usage logs?
Finally, given the different nature between the two search paradigms, the
ranking features typically used for Query-By-Keyword systems might not be
optimal for Query-By-Example. This paper describes our approach to solving
these challenges. We present experimental results confirming the effectiveness
of the proposed solution, particularly on query building and search ranking
tasks. As of writing this paper, the new system has been available to all
LinkedIn members
Learning to Rank Academic Experts in the DBLP Dataset
Expert finding is an information retrieval task that is concerned with the
search for the most knowledgeable people with respect to a specific topic, and
the search is based on documents that describe people's activities. The task
involves taking a user query as input and returning a list of people who are
sorted by their level of expertise with respect to the user query. Despite
recent interest in the area, the current state-of-the-art techniques lack in
principled approaches for optimally combining different sources of evidence.
This article proposes two frameworks for combining multiple estimators of
expertise. These estimators are derived from textual contents, from
graph-structure of the citation patterns for the community of experts, and from
profile information about the experts. More specifically, this article explores
the use of supervised learning to rank methods, as well as rank aggregation
approaches, for combing all of the estimators of expertise. Several supervised
learning algorithms, which are representative of the pointwise, pairwise and
listwise approaches, were tested, and various state-of-the-art data fusion
techniques were also explored for the rank aggregation framework. Experiments
that were performed on a dataset of academic publications from the Computer
Science domain attest the adequacy of the proposed approaches.Comment: Expert Systems, 2013. arXiv admin note: text overlap with
arXiv:1302.041
Simple to Complex Cross-modal Learning to Rank
The heterogeneity-gap between different modalities brings a significant
challenge to multimedia information retrieval. Some studies formalize the
cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal
embedding space to measure the cross-modality similarity. However, previous
methods often establish the shared embedding space based on linear mapping
functions which might not be sophisticated enough to reveal more complicated
inter-modal correspondences. Additionally, current studies assume that the
rankings are of equal importance, and thus all rankings are used
simultaneously, or a small number of rankings are selected randomly to train
the embedding space at each iteration. Such strategies, however, always suffer
from outliers as well as reduced generalization capability due to their lack of
insightful understanding of procedure of human cognition. In this paper, we
involve the self-paced learning theory with diversity into the cross-modal
learning to rank and learn an optimal multi-modal embedding space based on
non-linear mapping functions. This strategy enhances the model's robustness to
outliers and achieves better generalization via training the model gradually
from easy rankings by diverse queries to more complex ones. An efficient
alternative algorithm is exploited to solve the proposed challenging problem
with fast convergence in practice. Extensive experimental results on several
benchmark datasets indicate that the proposed method achieves significant
improvements over the state-of-the-arts in this literature.Comment: 14 pages; Accepted by Computer Vision and Image Understandin
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