10,881 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
Target Type Identification for Entity-Bearing Queries
Identifying the target types of entity-bearing queries can help improve
retrieval performance as well as the overall search experience. In this work,
we address the problem of automatically detecting the target types of a query
with respect to a type taxonomy. We propose a supervised learning approach with
a rich variety of features. Using a purpose-built test collection, we show that
our approach outperforms existing methods by a remarkable margin. This is an
extended version of the article published with the same title in the
Proceedings of SIGIR'17.Comment: Extended version of SIGIR'17 short paper, 5 page
Recommended from our members
Enriching videos with light semantics
This paper describes an ongoing prototypical framework to annotate and retrieve web videos with light semantics. The proposed framework reuses many existing vocabularies along with a video model. The knowledge is captured from three different information spaces (media content, context, document). We also describe ways to extract the semantic content descriptions from the existing usergenerated content using multiple approaches of linguistic processing and Named Entity Recognition, which are later identified with DBpedia resources to establish meanings for the tags. Finally, the implemented prototype is described with multiple search interfaces and retrieval processes. Evaluation on semantic enrichment shows a considerable (50% of videos) improvement in content description
AMC: Attention guided Multi-modal Correlation Learning for Image Search
Given a user's query, traditional image search systems rank images according
to its relevance to a single modality (e.g., image content or surrounding
text). Nowadays, an increasing number of images on the Internet are available
with associated meta data in rich modalities (e.g., titles, keywords, tags,
etc.), which can be exploited for better similarity measure with queries. In
this paper, we leverage visual and textual modalities for image search by
learning their correlation with input query. According to the intent of query,
attention mechanism can be introduced to adaptively balance the importance of
different modalities. We propose a novel Attention guided Multi-modal
Correlation (AMC) learning method which consists of a jointly learned hierarchy
of intra and inter-attention networks. Conditioned on query's intent,
intra-attention networks (i.e., visual intra-attention network and language
intra-attention network) attend on informative parts within each modality; a
multi-modal inter-attention network promotes the importance of the most
query-relevant modalities. In experiments, we evaluate AMC models on the search
logs from two real world image search engines and show a significant boost on
the ranking of user-clicked images in search results. Additionally, we extend
AMC models to caption ranking task on COCO dataset and achieve competitive
results compared with recent state-of-the-arts.Comment: CVPR 201
- …