LISTSERV mailing list manager LISTSERV 16.0

Help for SOCNET Archives


SOCNET Archives

SOCNET Archives


SOCNET@LISTS.UFL.EDU


View:

Message:

[

First

|

Previous

|

Next

|

Last

]

By Topic:

[

First

|

Previous

|

Next

|

Last

]

By Author:

[

First

|

Previous

|

Next

|

Last

]

Font:

Proportional Font

LISTSERV Archives

LISTSERV Archives

SOCNET Home

SOCNET Home

SOCNET  January 2018

SOCNET January 2018

Subject:

cfp Mining Social Networks for Local search and Location-based RecSys - Personal and Ubiquitous Computing, special issue, Springer

From:

Fabio Gasparetti <[log in to unmask]>

Reply-To:

Fabio Gasparetti <[log in to unmask]>

Date:

Wed, 17 Jan 2018 19:52:41 +0100

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (115 lines)

*****  To join INSNA, visit http://www.insna.org  *****

*** 2nd CFP - Apologies for cross-posting ***

MINING SOCIAL NETWORKS FOR LOCAL SEARCH AND LOCATION-BASED RECOMMENDER SYSTEMS 
A special issue of Personal and Ubiquitous Computing

Paper Submission Deadline: April 30, 2018


OBJECTIVES AND TOPICS 
+++++++++++++ 
Location-based services (LBSs) denote software-level services that use location
data in order to provide meaningful content to users or other services. The
proliferation of smartphones and wearable devices has increased the availability
of large amounts of spatio-temporal data (e.g., geolocation, motion and
environmental sensors) opens new opportunities and raises challenges as regards
the automatic discovery and interpretation of data in pervasive environments.
For instance, context-aware recommender systems (CARS) aggregate situational and
environmental information about people, places and activities to satisfy
immediate needs and offer enriched, situation-aware content and experiences.
Popular applications are tourist tour planning and music recommender systems.

While contextual factors quickly became the key of success of these pervasive
applications, information related to user interests and preferences as well as
social signals have not yet been adequately capitalized. The massive adoption of
social applications, including social network services (e.g. Facebook and
Twitter), collaborative tagging systems (e.g. Flickr and Delicious) and online
communities (e.g. Foursquare and Yelp) gathers a wealth of social interactions
between users, or between users and shared resources (e.g., points of interest,
movies). Social local search and recommendation often refers to the search and
recommendation paradigms affected by explicit or inferred social signals. The
former are identified in the user's personal circle of friends, relatives or
colleagues (egocentric network); the latter arise from groups of users that
share common interests and behaviors (sociocentric network), even if no explicit
ties bind them. Within this context, techniques employed for data and text
mining, social network analysis and community detection, sentiment analysis and
opinion mining have the chance to generate more accurate recommendations and
personalized services. For instance, they can help us understand more of users’
collective behavior by clustering similar users w.r.t. their interests,
preferences and activities; or by recognizing knowledge experts, namely, users
that are generally more capable than others of finding out relevant content.

The aim of this special issue is to explore recent advances in Local search (LS)
and location-based recommender systems (LRS) focusing on the value, impact and
implications of the analysis of social signals to alleviate information and
interaction overload by filtering the most attractive and relevant content. The
special issue solicits original research contributions from academia and
industry in the form of theoretical foundations, experimental and methodological
developments, comparative analyses, descriptive surveys, experiments and case
studies in the field.


Potential topics include, but are not limited to:

- Social network analysis and mining for LS and LRS
- Mining and modeling groups and communities for LS and LRS
- Addressing the cold-start problem in LS and LRS by leveraging social signals
- Temporal analysis of social networks for search and recommendation
- Extraction of contextual signals from user-generated content on social networks
- Opinion mining and sentiment analysis of user-generated content in LS and LRS
- Leveraging social signals for cross-domain search and recommendation 
- Group recommendation in LRS	
- Novel content-based and collaborative filtering approaches for social LS and LRS 
- Personality traits and factors in LS and LRS
- Trust, reputation and influence in LS and LRS 
- Explanation of recommendations for LS and LRS 
- Architectures for supporting large streams of socialdata & realtime applications
- Evaluation methods and datasets for social LS and LRS 
- Beyond accuracy: novelty, diversity, and serendipity of result sets
- Linked open data and LS and LRS
- Novel user interfaces for social LS and LRS
- Emerging applications that exploit social signals in LBS
- Security and privacy issues


GUEST EDITORS

- Fabio Gasparetti, Roma Tre University - Rome, Italy 
- Damianos Gavalas, University of the Aegean - Hermoupolis, Syros, Greece 
- Sergio Ilarri, University of Zaragoza - Zaragoza, Spain 
- Francesco Ricci, Free University of Bozen-Bolzano - Bozen-Bolzano, Italy 
- Zhiwen Yu, Northwestern Polytechnical University - Xi'an, Shaanxi, China



IMPORTANT DATES

Manuscript submission deadline: 30 April 2018 
First round review notification: 16 July 2018 
Revised manuscript submission deadline: 31 August 2018 
Final decision notification: 30 September 2018 
Expected publication (tentative): First quarter 2019

To submit a manuscript, the authors are required to follow the journal guidelines 
for manuscript submission. Each submission will undergo a peer review process.
Manuscripts should be submitted online: https://urldefense.proofpoint.com/v2/url?u=http-3A__www.springer.com_computer_hci_journal_779&d=DwIFaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=KPb4M59kBRLGKOQuA7X5YCBpsgXif8wbT9M4P4xT87Q&s=DvrpBkb6He7TcAXVQRdYWgE1-BkEBMZl277FfIDCZqo&e=  
Article type: "S.I.: Mining Social Network for Local search”

For enquiries please contact one of the guest editors.


ABOUT THIS JOURNAL

Personal and Ubiquitous Computing (Springer) is a peer-reviewed
multidisciplinary journal for researchers and educators who wish to understand
the implications of ubiquitous computing technologies and services. 
The Impact Factor for the journal is 2.395 according to the 
2016 Journal Citation Reports released by Thomson Reuters. 

_____________________________________________________________________
SOCNET is a service of INSNA, the professional association for social
network researchers (http://www.insna.org). To unsubscribe, send
an email message to [log in to unmask] containing the line
UNSUBSCRIBE SOCNET in the body of the message.

Top of Message | Previous Page | Permalink

Advanced Options


Options

Log In

Log In

Get Password

Get Password


Search Archives

Search Archives


Subscribe or Unsubscribe

Subscribe or Unsubscribe


Archives

June 2019
May 2019
April 2019
March 2019
February 2019
January 2019
December 2018
November 2018
October 2018
September 2018
August 2018
July 2018
June 2018
May 2018
April 2018
March 2018
February 2018
January 2018
December 2017
November 2017
October 2017
September 2017
August 2017
July 2017
June 2017
May 2017
April 2017
March 2017
February 2017
January 2017
December 2016
November 2016
October 2016
September 2016
August 2016
July 2016
June 2016
May 2016
April 2016
March 2016
February 2016
January 2016
December 2015
November 2015
October 2015
September 2015
August 2015
July 2015
June 2015
May 2015
April 2015
March 2015
February 2015
January 2015
December 2014
November 2014
October 2014
September 2014
August 2014
July 2014
June 2014
May 2014
April 2014
March 2014
February 2014
January 2014
December 2013
November 2013
October 2013
September 2013
August 2013
July 2013
June 2013
May 2013
April 2013
March 2013
February 2013
January 2013
December 2012
November 2012
October 2012
September 2012
August 2012
July 2012
June 2012
May 2012
April 2012
March 2012
February 2012
January 2012
December 2011
November 2011
October 2011
September 2011
August 2011
July 2011
June 2011
May 2011
April 2011
March 2011
February 2011
January 2011
December 2010
November 2010
October 2010
September 2010
August 2010
July 2010
June 2010
May 2010
April 2010
March 2010
February 2010
January 2010
December 2009
November 2009
October 2009
September 2009
August 2009
July 2009
June 2009
May 2009
April 2009
March 2009
February 2009
January 2009
December 2008
November 2008
October 2008
September 2008
August 2008
July 2008, Week 62
July 2008
June 2008
May 2008
April 2008
March 2008
February 2008
January 2008
December 2007
November 2007
October 2007
September 2007
August 2007
July 2007
June 2007
May 2007
April 2007
March 2007
February 2007
January 2007
December 2006
November 2006
October 2006
September 2006
August 2006
July 2006
June 2006
May 2006
April 2006
March 2006
February 2006
January 2006
December 2005
November 2005
October 2005
September 2005
August 2005
July 2005
June 2005
May 2005
April 2005
March 2005
February 2005
January 2005
December 2004
November 2004
October 2004
September 2004
August 2004
July 2004
June 2004
May 2004
April 2004
March 2004
February 2004
January 2004
December 2003
November 2003
October 2003
September 2003
August 2003
July 2003
June 2003
May 2003
April 2003
March 2003
February 2003
January 2003
December 2002
November 2002
October 2002
September 2002
August 2002
July 2002
June 2002
May 2002
April 2002
March 2002
February 2002
January 2002
December 2001
November 2001
October 2001
September 2001
August 2001
July 2001
June 2001
May 2001

ATOM RSS1 RSS2



LISTS.UFL.EDU

CataList Email List Search Powered by the LISTSERV Email List Manager