2,850 research outputs found
Reply With: Proactive Recommendation of Email Attachments
Email responses often contain items-such as a file or a hyperlink to an
external document-that are attached to or included inline in the body of the
message. Analysis of an enterprise email corpus reveals that 35% of the time
when users include these items as part of their response, the attachable item
is already present in their inbox or sent folder. A modern email client can
proactively retrieve relevant attachable items from the user's past emails
based on the context of the current conversation, and recommend them for
inclusion, to reduce the time and effort involved in composing the response. In
this paper, we propose a weakly supervised learning framework for recommending
attachable items to the user. As email search systems are commonly available,
we constrain the recommendation task to formulating effective search queries
from the context of the conversations. The query is submitted to an existing IR
system to retrieve relevant items for attachment. We also present a novel
strategy for generating labels from an email corpus---without the need for
manual annotations---that can be used to train and evaluate the query
formulation model. In addition, we describe a deep convolutional neural network
that demonstrates satisfactory performance on this query formulation task when
evaluated on the publicly available Avocado dataset and a proprietary dataset
of internal emails obtained through an employee participation program.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on
Information and Knowledge Management. 201
Constructing an Interaction Behavior Model for Web Image Search
User interaction behavior is a valuable source of implicit relevance
feedback. In Web image search a different type of search result presentation is
used than in general Web search, which leads to different interaction
mechanisms and user behavior. For example, image search results are
self-contained, so that users do not need to click the results to view the
landing page as in general Web search, which generates sparse click data. Also,
two-dimensional result placement instead of a linear result list makes browsing
behaviors more complex. Thus, it is hard to apply standard user behavior models
(e.g., click models) developed for general Web search to Web image search.
In this paper, we conduct a comprehensive image search user behavior analysis
using data from a lab-based user study as well as data from a commercial search
log. We then propose a novel interaction behavior model, called grid-based user
browsing model (GUBM), whose design is motivated by observations from our data
analysis. GUBM can both capture users' interaction behavior, including cursor
hovering, and alleviate position bias. The advantages of GUBM are two-fold: (1)
It is based on an unsupervised learning method and does not need manually
annotated data for training. (2) It is based on user interaction features on
search engine result pages (SERPs) and is easily transferable to other
scenarios that have a grid-based interface such as video search engines. We
conduct extensive experiments to test the performance of our model using a
large-scale commercial image search log. Experimental results show that in
terms of behavior prediction (perplexity), and topical relevance and image
quality (normalized discounted cumulative gain (NDCG)), GUBM outperforms
state-of-the-art baseline models as well as the original ranking. We make the
implementation of GUBM and related datasets publicly available for future
studies.Comment: 10 page
Conventions and mutual expectations — understanding sources for web genres
Genres can be understood in many different ways. They are often perceived as a primarily sociological construction, or, alternatively, as a stylostatistically observable objective characteristic of texts. The latter view is more common in the research field of information and language technology. These two views can be quite compatible and can inform each other; this present investigation discusses knowledge sources for studying genre variation and change by observing reader and author behaviour rather than performing analyses on the information objects themselves
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
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