7,869 research outputs found
Anticipating Information Needs Based on Check-in Activity
In this work we address the development of a smart personal assistant that is
capable of anticipating a user's information needs based on a novel type of
context: the person's activity inferred from her check-in records on a
location-based social network. Our main contribution is a method that
translates a check-in activity into an information need, which is in turn
addressed with an appropriate information card. This task is challenging
because of the large number of possible activities and related information
needs, which need to be addressed in a mobile dashboard that is limited in
size. Our approach considers each possible activity that might follow after the
last (and already finished) activity, and selects the top information cards
such that they maximize the likelihood of satisfying the user's information
needs for all possible future scenarios. The proposed models also incorporate
knowledge about the temporal dynamics of information needs. Using a combination
of historical check-in data and manual assessments collected via crowdsourcing,
we show experimentally the effectiveness of our approach.Comment: Proceedings of the 10th ACM International Conference on Web Search
and Data Mining (WSDM '17), 201
A personalized and context-aware news offer for mobile devices
For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer
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
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
An integrated ranking algorithm for efficient information computing in social networks
Social networks have ensured the expanding disproportion between the face of
WWW stored traditionally in search engine repositories and the actual ever
changing face of Web. Exponential growth of web users and the ease with which
they can upload contents on web highlights the need of content controls on
material published on the web. As definition of search is changing,
socially-enhanced interactive search methodologies are the need of the hour.
Ranking is pivotal for efficient web search as the search performance mainly
depends upon the ranking results. In this paper new integrated ranking model
based on fused rank of web object based on popularity factor earned over only
valid interlinks from multiple social forums is proposed. This model identifies
relationships between web objects in separate social networks based on the
object inheritance graph. Experimental study indicates the effectiveness of
proposed Fusion based ranking algorithm in terms of better search results.Comment: 14 pages, International Journal on Web Service Computing (IJWSC),
Vol.3, No.1, March 201
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