[UAI] [journals] CFP: Special Issue on Agricultural and Field Robotics in /Robotics/ - an open access journal by MDPI

[Linda Wang]

Dear Colleagues,

Robotics is currently running a Special Issue entitled "Agricultural and
Field Robotics". Prof. Dr. Qin Zhang and Prof. Dr. Manoj Karkee, both
from Washington State University, are serving as Guest Editors for this
issue. We would like to invite you to contribute based on your expertise.

Agriculture is one of the oldest and most important industries that
human civilization has established. Fundamentally, agriculture relies on
the efficient utilization of natural resources, such as land, water,
nutrition, and other chemicals to produce the basic necessities of human
lives, including food, fiber, feed, and fuel. As predicted by the United
Nations, the world’s population will increase to approximately 10
billion by 2050. The continuously increasing pressure to feed a rapidly
growing population presents a huge challenge to the agricultural
industry: How to sustainably produce enough agricultural supplies to
meet such a huge demand.

For further reading, please follow the link to the Special Issue website
at: http://www.mdpi.com/journal/robotics/special_issues/AFR.

The submission deadline is 31 July 2018. You may send your manuscript
now or up until the deadline. Submitted papers should not be under
consideration for publication elsewhere. We also encourage authors to
send a short abstract or tentative title to the Editorial Office in
advance (robot...@mdpi.com).

From Volume 6, all published papers will be added in Scopus. The journal
is also indexed in Web of Science (Thomson Reuters), as part of the ESCI
database.

Robotics is fully open access. Open access (unlimited and free access by
readers) increases publicity and promotes more frequent citations, as
indicated by several studies. Open access is supported by the authors
and their institutes. An article processing charge (APC) of CHF 350
currently applies to all accepted papers. You may be entitled to a
discount if you have previously received a discount code or if your
institute is participating in the MDPI Institutional Open Access Program
(IOAP), for more information see: http://www.mdpi.com/about/ioap.

For further details on the submission process, please see the
instructions for authors at the journal website
(http://www.mdpi.com/journal/robotics/instructions).

We look forward to hearing from you.

Best regards,
Linda Wang
Managing Editor

Robotics (ISSN 2218-6581; http://www.mdpi.com/journal/robotics) is a
journal published by MDPI AG, Basel, Switzerland. Robotics maintains
rigorous peer-review and a rapid publication process. All articles are
published with a CC BY 4.0 license. For more information on the CC BY
license, please see: http://creativecommons.org

To submit to the journal click here:
http://susy.mdpi.com/user/manuscripts/upload?journal=robotics

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MDPI
Multidisciplinary Digital Publishing Institute
www.mdpi.com

St. Alban-Anlage 66
4052 Basel
Switzerland

Tel. +41 61 683 77 34
Fax +41 61 302 89 18


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[UAI] Final CFP: ISWC'18 workshop on Ontology Matching (OM-2018), submission deadline is approaching on June 4th, 2018

[Pavel Shvaiko]

Apologies for cross-postings


--
 CALL FOR CONTRIBUTIONS
THE SUBMISSION DEADLINE IS APPROACHING
 IN TWO WEEKS ON JUNE 4TH, 2018

--

The Thirteenth International Workshop on
ONTOLOGY MATCHING
(OM-2018)
 http://om2018.ontologymatching.org/
  October 8th, 2018, ISWC Workshop Program,
  Monterey, CA, US


BRIEF DESCRIPTION AND OBJECTIVES
Ontology matching is a key interoperability enabler for the Semantic Web,
as well as a useful technique in some classical data integration tasks
dealing with the semantic heterogeneity problem. It takes ontologies
as input and determines as output an alignment, that is, a set of
correspondences between the semantically related entities of those
ontologies.
These correspondences can be used for various tasks, such as ontology
merging, data interlinking, query answering or process mapping.
Thus, matching ontologies enables the knowledge and data expressed
in the matched ontologies to interoperate.


The workshop has three goals:
1.
To bring together leaders from academia, industry and user institutions
to assess how academic advances are addressing real-world requirements.
The workshop will strive to improve academic awareness of industrial
and final user needs, and therefore, direct research towards those needs.
Simultaneously, the workshop will serve to inform industry and user
representatives about existing research efforts that may meet their
requirements. The workshop will also investigate how the ontology
matching technology is going to evolve, especially with respect to
data interlinking, process mapping and web table matching tasks.

2.
To conduct an extensive and rigorous evaluation of ontology matching
and instance matching (link discovery) approaches through
the OAEI (Ontology Alignment Evaluation Initiative) 2018 campaign:
http://oaei.ontologymatching.org/2018/

3. To examine new uses, similarities and differences from database
schema matching, which has received decades of attention
but is just beginning to transition to mainstream tools.

This year, in sync with the main conference, we encourage submissions
specifically devoted to: (i) datasets, benchmarks and replication studies,
services, software, methodologies, protocols and measures
(not necessarily related to OAEI), and (ii) application of
the matching technology in real-life scenarios and assessment
of its usefulness to the final users.



TOPICS of interest include but are not limited to:
Business and use cases for matching;
Requirements to matching from specific application scenarios;
Application of matching techniques in real-world scenarios;
Formal foundations and frameworks for matching;
Matching and big data;
Matching and linked data;
Instance matching, data interlinking and relations between them;
Process model matching;
Large-scale and efficient matching techniques;
Matcher selection, combination and tuning;
User involvement (including both technical and organizational aspects);
Explanations in matching;
Social and collaborative matching;
Uncertainty in matching;
Reasoning with alignments;
Alignment coherence and debugging;
Alignment management;
Matching for traditional applications (e.g., information integration);
Matching for emerging applications (e.g., search, web-services).



SUBMISSIONS
Contributions to the workshop can be made in terms of technical papers and
posters/statements of interest addressing different issues of ontology
matching
as well as participating in the OAEI 2018 campaign. Technical papers should
be not longer than 12 pages using the LNCS Style:
http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0
Posters/statements of interest should not exceed 2 pages and
should be handled according to the guidelines for technical papers.
All contributions should be prepared in PDF format and should be submitted
through the workshop submission site at:

https://www.easychair.org/conferences/?conf=om2018

Contributors to the OAEI 2018 campaign have to follow the campaign
conditions
and schedule at http://oaei.ontologymatching.org/2018.



DATES FOR TECHNICAL PAPERS AND POSTERS:
June 4th, 2018: Deadline for the submission of papers.
June 27th, 2018: Deadline for the notification of acceptance/rejection.
July 31st, 2018: Workshop camera ready copy submission.
October 8th, 2018: OM-2018, Monterey, CA, US.

Contributions will be refereed by the Program Committee.
Accepted papers will be published in the workshop proceedings as a volume
of CEUR-WS
as well as indexed on DBLP.

The extended versions of the best technical papers of the workshop will be
invite

[UAI] PhD positions at University of Namur

[Benoit Frenay]

*** Several PhD studentships are available on ***
*** Software engineering and software quality assurance for learning 
systems ***


University of Namur // Namur Digital Institute (NADI)

Supervisors: Patrick Heymans, Pierre-Yves Schobbens, Benoît Frenay and 
Gilles Perrouin.


The studentships are available immediately. Applications are 
continuously reviewed until positions are filled.


Positions involve developing and studying solutions to improve the 
engineering and quality assurance (testing, verification...) of software 
that learns and adapts its behavior accordingly. The successful 
candidates will join the stimulating research environment provided by 
NADI and the VeriLearn project, an ambitious inter-university project 
funded by the Belgian research foundations (FNRS and FWO) and conducted 
jointly with KUL (team of Luc De Raedt) and ULB (team of Jean-François 
Raskin).


We are looking for motivated candidates, determined to graduate within 4 
years and to publish their results in the best conferences and 
journals. Candidates should hold a Master or Engineering degree in 
Computer Science, or equivalent. They should have affinity with one or 
more of the following topics: software engineering, quality assurance 
(testing, verification...), formal methods and machine learning.


PhD students earn between 1.900 and 2.000 EUR net salary per month.

Positions are based in Namur, Belgium. Namur is the capital of Wallonia, 
the french-speaking region of Belgium.  It is a charming and 
affordable midsize city situated 45 min south from Brussels and within 3 
hours from the Paris, London, Amsterdam and Bonn.


For more information on the research and the position, please contact 
patrick.heym...@unamur.be , including 
a copy of your CV if possible.  Formal applications require providing 
(1) a full CV (including a publication list if any), (2) a cover letter 
of max 2 pages describing your skills, experience, and why you are well 
suited to the position, (3) a transcript with the grades obtained for 
each course you took on each university year, (4) optional 
recommendation letters (up to three). Please use ’'VERILEARN 
APPLICATION'’ as the subject of your email.


We promote diversity in employment and welcome applications from all 
sections of the community.


--

Benoît FRÉNAY
Associate Professor
Faculty of Computer Science

T. +32 (0)81 724 976 (secr. 725 252)
F. +32 (0)81 724 967
benoit.fre...@unamur.be 

Université de Namur ASBL
Rue de Bruxelles 61 - 5000 Namur

Let’s respect the environment together.
Only print this message if necessary!

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[UAI] CFP - HAI 2018: 6th International Conference on Human-Agent Interaction

[Tran-Thanh L.]

FIRST CALL FOR PAPERS

* 6th International Conference on Human-Agent Interaction (HAI 2018) *

  *** Southampton, UK  --- 15-18 December 2018 ***

 http://hai-conference.net/hai2018

HAI 2018 is the 6th annual International Conference on Human-Agent
Interaction. It aims to be the premier interdisciplinary venue for
discussing and disseminating state-of-the-art research and results
that reach across conventional interaction boundaries from people to a
wide range of intelligent systems, including physical robots, software
agents and digitally-mediated human-human communication. HAI focusses
on technical as well as social aspects.

The theme for HAI 2018 is *Trustworthy Interaction*. During the last
decade, much research has been devoted to increasing the interaction
quality between humans and artificial intelligent agents. At the same
time, more and more intelligent systems are generating decisions,
either autonomously or with people "in-the-loop". Many questions
arise when considering the trustworthiness of intelligent systems,
operating on their own or in collaboration with people. Do users
trust these systems? Do the systems trust people? How can systems
gain users' trust, and vice versa? What happens if trust is lost?
How is trust modelled? How is trust evaluated?

As robots and agents enter our everyday lives, interactions will
require more initiative and flexibility from agents, i.e., greater
autonomy and intelligence; however, outcomes may be less predictable.
Current research trends which emphasise statistical behaviour models
constructed from observations may only capture rather shallow
structures and may overlook more complex aspects that underly less
restricted exchanges. We strive for better understanding and models
of interaction principles that take into account not only situational
and task aspects but also detailed user models. More research is
necessary to lead the way towards deeper and more robust models of
human-agent interaction. HAI encourages contributions that tackle
these questions in realistic, practical settings.

The HAI conference seeks contributions from a broad range of fields
spanning engineering, computer science, psychology and sociology,
covering diverse areas, including:

- human-robot interaction;
- intelligent systems;
- affective computing;
- computer-supported collaborative work;
- gaming/serious games; and
- artificial intelligence.

Topics include, but are not limited to:

- studies of Human-Agent Interaction, with quantitative/qualitative results;
- theoretical models;
- technological advances;
- experimental methods;
- impacts of embodiment;
- character and avatar design; and
- agents in social networks.

Results that have implications to the broader HAI communities are
encouraged (e.g., HRI, human-virtual agent interaction, and
interaction with smart homes and autonomous vehicles), as well as
position papers and preliminary, high-impact studies.

Full papers, late-breaking results, demo papers and tutorial/workshop
overviews will be published in archival format. A Journal Special Issue
will be produced with select extended papers from the conference.


IMPORTANT DATES.

* Full Papers (up to 8 pages) *
02 July 2018 - Abstract Submission
09 July 2018 - Full-Paper Submission
27 August 2018 - Notification

* Workshop Proposals *
16 July 2018 - Proposal Submission
30 July 2018 - Notification

* Tutorial Proposals *
30 July 2018   - Proposal Submission
13 August 2018 - Notification

* Late-breaking & Demo Papers (2 pages) *
1 October 2018 - Short-Paper Submission
22 October 2018 - Notification

* Conference in Southampton, UK *
15-16 December 2018 - Workshops, Tutorials
16-18 December 2018 - Main Programme


LOCATION.

HAI will take place at the University of Southampton
(https://www.southampton.ac.uk).
Southampton is a vibrant port city located in the south of England,
rich in history and culture, and easily reached from London airports.


ORGANISING COMMITTEE.

General Co-Chairs:
-- Michita Imai, Keio University, Japan
-- Tim Norman, University of Southampton, UK

Program Chairs:
-- Elizabeth Sklar, King's College London, UK
-- Takanori Komatsu, Meiji University, Japan

Workshops Chair:
-- Marc Hanheide, University of Lincoln, UK

Tutorials Chair:
-- Wolmet Barendregt, Univ of Gothenburg, Sweden

Local Chairs:
-- Long Tran-Thanh, Univ of Southampton, UK
-- Theodora (Lela) Koulouri, Brunel University, UK

Sponsorships Chair:
-- Masahiko Osawa, Keio University, Japan

Finance Chair:
-- Sebastian Stein, University of Southampton, UK

Publicity Chair:
-- Mohammad Obaid, Uppsala University, Sweden

Publications Chair:
-- Avi Rosenfeld, Jerusalem College of Tech, Israel

Posters Chair:
-- Tin Leelavimolsilp, Univ of Southampton, UK

Web Chair:
-- Nhat Truong, University of Southampton, UK


PROGRAM COMMITTEE.
M. Q. Azhar, City University of New York, USA
Paul Baxter, University of Lincoln, UK
Tony Belpaeme, University of Plymouth, UK
Oya Celiktutan D

[UAI] 21st International Conference on Discovery Science (DS 2018): Second Call for Papers

[George Angelos Papadopoulos]

*** SECOND CALL FOR PAPERS ***

21st International Conference on Discovery Science (DS 2018)

St. Raphael Resort, Limassol, Cyprus, 30-31 October, 2018

http://www.cyprusconferences.org/ds2018

(*** In conjunction with ISMIS 2018 ***)


WELCOME

The 21st International Conference on Discovery Science (DS 2018) provides
an open forum for intensive discussions and exchange of new ideas among
researchers working in the area of Discovery Science. 

The scope of the conference includes the development and analysis of
methods for discovering scientific knowledge, coming from machine
learning, data mining, intelligent data analysis, big data analysis as well as
their application in various scientific domains. 

We welcome papers that focus on the analysis of different types of massive
and complex data, including structured, spatio-temporal and network data.
We particularly welcome papers addressing applications. Finally, we would
like to encourage contributions from the areas of computational
scientific discovery, mining scientific data, computational creativity and
discovery informatics. 

DS 2018 will be co-located with ISMIS 2018, the 24th International
Symposium on Methodologies for Intelligent Systems. The two conferences
will be held in parallel, and will share their invited talks. 


TOPICS

We invite submissions of research papers addressing all aspects of
discovery science. We particularly welcome contributions that discuss the
application of data analysis, data mining and other support techniques for
scientific discovery including, but not limited to, biomedical, astronomical
and other physics domains. Applications to massive, heterogeneous,
continuous or imprecise data sets are of particular interests. Possible topics
include, but are not limited to: 

• Knowledge discovery, machine learning and statistical methods 
• Ubiquitous knowledge discovery 
• Data streams, evolving data and models 
• Change detection and model maintenance 
• Active knowledge discovery 
• Learning from text and web mining 
• Information extraction from scientific literature 
• Knowledge discovery from heterogeneous, unstructured and
   multimedia data 
• Knowledge discovery in network and link data 
• Knowledge discovery in social networks 
• Data and knowledge visualization 
• Spatial/temporal Data 
• Mining graphs and structured data 
• Planning to learn 
• Knowledge transfer 
• Computational creativity 
• Human-machine interaction for knowledge discovery and management 
• Biomedical knowledge discovery and analysis 
• Machine learning for high-performance computing, grid and
   cloud computing 
• Applications of the above techniques to natural or social sciences 


PAPER SUBMISSION

Papers may contain up to fifteen (15) pages and must be formatted
according to the layout supplied by Springer-Verlag for the Lecture Notes
in Computer Science series. Submitted papers may not have appeared in or
be under consideration for another workshop, conference or a journal,
nor may they be under review or submitted to another forum during the DS
2018 review process

Authors can submit their papers electronically via our submission page
through Easychair: https://easychair.org/conferences/?conf=ds20180 .


PUBLICATION AND JOURNAL SPECIAL ISSUES

The DS 2018 proceedings will be published by Springer in LNAI
(Lecture Notes in Artificial Intelligence) and will be available at the
conference.

Authors of best papers will be invited to submit their extended versions to
the Machine Learning journal (https://link.springer.com/journal/10994)
published by Springer. Fast Track Processing will be used to have them
reviewed and published.


IMPORTANT DATES

• Paper Submissions due: 18th June 2018
• Notification of Acceptance/Rejection: 18th July 2018
• Camera-Ready Versions of Accepted Papers: 28th July 2018
• Author Registration: 31st Juy 2018
• Early Non-Author Registration: 10th September 2018
• Late Non-Author Registration: after 10th September 2018


ORGANIZING COMMITTEE

General Chair
• George Angelos Papadopoulos (University of Cyprus, Cyprus)

Program Committee Co-Chairs
• Larisa Soldatova, Goldsmiths, University of London, UK
• Joaquin Vanschoren, Eindhoven University of Technology, the Netherlands

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[UAI] Open positions in Climate Informatics group German Aerospace Center Jena

[Jakob Runge]

Dear All

The Institute of Data Science in Jena, founded by the German Aerospace
Center (DLR), offers two well-funded Postdoc/PhD positions in the
Climate Informatics group that I am leading. The group develops
innovative data science methods (based on graphical models, causal
inference, nonlinear dynamics, deep learning) to address key problems
in the climate sciences.

I am happy to answer any questions, we especially encourage female
applicants! More info on the group currently at www.climateinformaticsl
ab.com

Feel free to forward via mailing lists and to potential candidates.

Best wishes,
Jakob Runge
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[UAI] Postdoc in Multi-Agent Reinforcement Learning at Northeastern University (updated)

[Amato, Chris]

*Updated to include the application link and I extend the deadline a few days*

This project will explore methods for multi-agent reinforcement learning for a 
team of aerial robots in dynamic environments. The post-doc will focus on 
developing learning methods, but will coordinate with other students, post-docs 
and professors to test the approaches with aerial robots.

For full consideration *please apply by May 30th*

Qualifications:

PhD in Computer Science or related field
Top-tier publications in the general area of multi-agent reinforcement learning 
(broadly defined)
Programming experience in a high-level language such as C++ or python
Self-motivated
Works well with a team

Knowledge of the following is helpful, but not required: deep reinforcement 
learning, decentralized POMDPs or multi-robot learning.

The position will be for up to 2 years and will be supervised by Chris Amato 
(http://www.ccs.neu.edu/home/camato/) at Northeastern University.

Northeastern University is located in the heart of Boston, a city with one of 
the richest research environments in the world, with over 10K researchers, 50K 
graduate students, and a top startup community. Northeastern University is home 
to 35,000 full- and part-time degree students. The past decade has witnessed a 
dramatic increase in Northeastern’s international reputation for research and 
innovative educational programs (according to 
CSRankings.org, Northeastern is ranked 19th in the USA 
overall and 26th in robotics). For more information about Northeastern and the 
College of Computer and Information Science, please visit 
http://www.ccis.northeastern.edu/

To apply, please upload a CV and either two reference letters or contact 
information for two references to the link below. 
Feel free to also email me if you want more info.

https://neu.peopleadmin.com/postings/54739

  Chris

Christopher Amato
Assistant Professor
College of Computer and Information Science (CCIS)
Northeastern University
http://www.ccs.neu.edu/home/camato/






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[UAI] RW 2018 Summer School - Third Call for Applications

[Amal TAWAKULI]

===
Third Call for Applications: The 14th International Reasoning Web Summer School 
- RW 2018
===

Luxembourg, 22 - 25 Sep 2018
http://2018.ruleml-rr.org/rw.html

==Summary==
The Reasoning Web Summer School 2018, as part of the annual series of summer 
schools, welcomes applications from Master and PhD students as well as from 
postdoctoral researchers, young researchers, and senior researchers wishing to 
learn about the research areas of Semantic Web and related sub-areas such as 
Linked Data, Knowledge Graphs, Ontologies, Rules, and Logic.

Participants to the summer school will have the opportunity to attend lectures, 
to be involved in working groups concerning small case studies or research 
tasks to focus on and to be presented in a final plenary session, discuss ideas 
and closely interact with leading researchers in the Semantic Web community and 
beyond.

==Program==
This year’s school program will include the following topics and lecturers:

• Information Extraction for Knowledge Graph Construction - Philipp Cimiano, 
University of Bielefeld
• Semantic Data in the Cloud - Steffen Staab and Daniel Janke, University of 
Koblenz Landau
• Machine Learning with and for Knowledge Graphs - Heiko Paulheim, University 
of Mannheim
• Stream Reasoning - Emanuele Della Valle, Politecnico di Milano
• Learning and Reasoning in Knowledge Graphs - Daria Stepanova, Max Planck 
Institute
• Normative reasoning for the Semantic Web - Guido Gorvernatori, Commonwealth 
Scientific and Industrial Research Organisation
• Efficient SPARQL queries on very large Knowledge Graphs - Hannah Bast, 
University of Freiburg
• Reasoning at Scale -  Jacopo Urbani, Vrije Universiteit Amsterdam
• Deep Learning for the Semantic Web - Thomas Lukasiewicz, University of Oxford

Although no specific background knowledge is required for attending the summer 
school, basics of Knowledge Representation and the Semantic Web (including 
technologies such as RDF, OWL, etc.) will be helpful for benefiting from the 
contents of the school. Students are also committed to a full participation for 
the whole duration of the school.

==Registration Fees==
The registration fees for attending the RW Summer School will be 300 EUR (if 
your RW registration is bundled with the one for RuleML-RR) and 350 EUR (if you 
choose to only register for RW), respectively. This includes all lectures and 
teaching sessions, lunches, coffee breaks, school proceedings and a social 
event.

==Student Grants==
A limited number of grants will be available for selected students who would 
otherwise not be able to attend the summer school. As such, please ask for 
financial support in your application (see “Application” section) only if this 
is the necessary condition for you to participate in the summer school.

==Application==
Students and researchers interested in participating need to submit their 
application by June 30, 2018 via EasyChair using the following link: 
https://easychair.org/conferences/?conf=rw2018.

The application should consist of a single pdf file that includes:
• A single-page CV.
• A short motivation letter of up to 300 words stating research interests and 
reasons for the willingness to attend the summer school.
• Necessity for a visa to enter Luxembourg (Yes or No). If required, invitation 
letters will be set up.
• Necessity for financial support. A limited number of travel grants will be 
provided, please thus ask for financial support only if this is a necessary 
condition for you to join the school. Please add a rough quote for your 
estimated expenses (travel + hotel costs) to your application in this case.

Notifications about the selection results will be sent by July 15, 2018

For any questions, please contact: rw2...@easychair.org

RW2018 Chairs
Claudia d’Amato and Martin Theobald



Schéi Gréiss | Mit Freundlichen Grüßen | Meilleures Salutations | With Kind 
Regards

Amal Tawakuli
Doctoral Candidate
Big Data and Data Science Research Group - ILIAS Lab - CSC Research Unit

UNIVERSITÉ DU LUXEMBOURG

Campus Belval
6, avenue de la Fonte
L-4364 Esch-sur-Alzette/Belval
T +352 46 66 44 9811
amal.tawak...@uni.lu
www.uni.lu
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[UAI] CFP: Intelligent Recommender Systems by Knowledge Transfer & Learning @ RecSys

[Shaghayegh Sahebi]

recsysktl 2018 : The 2nd Workshop on Intelligent Recommender Systems by
Knowledge Transfer & Learning
https://recsysktl.wordpress.com/

Held in conjunction with the ACM Conference on Recommender Systems (RecSys
2018).
2nd-7th October 2018, Vancouver, Canada

*Submission deadline*: July 16th, 2018

Submission via: https://easychair.org/conferences/?conf=recsysktl2018

Recommender systems provide relevant items and information to users by
profiling users and items in various ways. Growth of online information
systems has led to an abundance of data that is heterogeneous, noisy, and
changes rapidly. The data used by recommender systems, in forms of implicit
or explicit user feedback, follow the same trend: the feedback can be in
various formats, such as ratings, online behaviors, or textual reviews, and
collected from multiple resources, the collected feedback is uncertain, and
user taste and item popularities can change over time. In this workshop,
the focus is on recommender systems’ data heterogeneity: collected feedback
with various types, collected from various domains, contexts, or
applications.

While the data heterogeneity provides multiple views to users’ preferences,
and thus, may be helpful in recommending more related items to users, it
may also add more noise and uncertainty to the data and lead to weaker
recommendations. Cross-domain recommender systems and transfer learning
approaches propose to effectively take advantage of such diversity of
viewpoints to provide better-quality recommendations and resolve issues
such as the cold-start problem.

The emerging research on cross-domain, context-aware and multi-criteria
recommender systems, has proved to be successful. Given the recent
availability of cross-domain datasets and novelty of the topic, we organize
the 2nd workshop on intelligent recommender systems by knowledge transfer
and learning (RecSysKTL) held in conjunction with ACM RecSys 2018. This
workshop intends to create a medium to generate more practical and
efficient predictive models or recommendation approaches by leveraging user
feedbacks or preferences from multiple domains.

---

Generally, we focus on the topic of “cross-domain”, where the notion of
“domain” may vary from applications to applications. For example, the
concept of context-aware and multi-criteria recommender systems can also be
considered as an application of “cross-domain” techniques. Particularly, we
are interested in how to apply knowledge transfer and learning approaches
to build intelligent recommender systems. Domain could be (but not limited
to):

* From one application to another: We may utilize user behaviors on social
networks to predict their preferences on movies (e.g., Netflix, Youtube) or
music (e.g., Pandora, Spotify).
* From one category to another: We may predict a user’s taste on
electronics by using his or her preference history on books based on the
data collected from Amazon.com.
* From one context to another: We may collect a user’s preferences on the
items over different time segment (e.g., weekend or weekday) and predict
her preferences on movie watching within another context (e.g., companion
and location).
* From one task to another: It may be useful for us to predict how a user
will select hotels for his or her vocations by learning from how he or she
books the tickets for transportations.
* From one structure to another: It could be also possible for us to infer
social connections by learning from the structure of heterogeneous
information networks.
The topics of interest for this workshop include (but are not limited to):

* Applications of Knowledge Transfer for Recommender Systems
* Cross-domain recommendation
* Context-aware recommendation, time-aware recommendation
* Multi-criteria recommender systems
* Novel applications
* Methods for Knowledge Transfer in Recommender Systems
* Knowledge transfer for content-based filtering
* Knowledge transfer in user- and item-based collaborative filtering
* Transfer learning of model-based approaches to collaborative filtering
* Deep Learning methods for knowledge transfer
* Challenges in Knowledge Transfer for Recommendation
* Addressing user feedback heterogeneity from multiple domains (e.g.
implicit vs. explicit, binary vs. ratings, etc.)
* Multi-domain and multi-task knowledge representation and learning
* Detecting and avoiding negative (non-useful) knowledge transfer
* Ranking and selection of auxiliary sources of knowledge to transfer from
* Performance and scalability of knowledge transfer approaches for
recommendation
* Evaluation of Recommender Systems based on Knowledge Transfer
* Beyond accuracy: novelty, diversity, and serendipity of recommendations
supported by the transfer of knowledge
* Performance of knowledge transfer systems in cold-start scenarios
* Impact of the size and quality of transferred data on target
recommendations
* Analy