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recsysktl 2018 : The 2nd Workshop on Intelligent Recommender Systems by
Knowledge Transfer & Learning
https://urldefense.proofpoint.com/v2/url?u=https-3A__recsysktl.wordpress.com_&d=DwIFaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=pWfBBPxksaVfN-jqQNe5smBuURWJH06b0vzlVSbFYDk&s=4LpR15MNyCa4-NHxkqq9FIyl6BbWVynmZqjUYgntjBs&e=

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://urldefense.proofpoint.com/v2/url?u=https-3A__easychair.org_conferences_-3Fconf-3Drecsysktl2018&d=DwIFaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=pWfBBPxksaVfN-jqQNe5smBuURWJH06b0vzlVSbFYDk&s=SI5eh6-k8yneXGSdVPPvAhBGHwKe1JDKSPThCVvQ3io&e=

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.

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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
* Analysis of the amount of domain overlap on recommendation performance

Submissions Guidelines
We accept long papers (up to 8 pages) and short papers (up to 4 pages) in
ACM conference format (references are counted in the page limit). Long
papers are expected to present original research work which should report
on substantial contributions of lasting value. Short papers may discuss the
late-breaking results or exciting new work that is not yet mature, or open
challenges in promising research directions. The accepted papers will be
invited for presentations and the proceedings will be available at
https://urldefense.proofpoint.com/v2/url?u=http-3A__ceur-2Dws.org&d=DwIFaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=pWfBBPxksaVfN-jqQNe5smBuURWJH06b0vzlVSbFYDk&s=USEibtzqlUTxBTnVY-3a1ZtVqDJK-0ASFG-LdJKpLuc&e=, while the authors will hold the copyrights.

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