Call For Papers

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 http://ceur-ws.org, while the authors will hold the copyrights.

Note: the review process is double-blind. All submissions must be appropriately anonymized according to the following guidelines:

  1. Author’s names and affiliations are not visible anywhere in the paper.
  2. Acknowledgements should be anonymized or removed during the review process.
  3. Self-citations should be included where necessary, but must use the third person. For example, “… as shown in our previous user study [2] … ” is not allowed, whereas “… as shown in Smith et al. [2] ” is acceptable (because in this case the citation [2] will NOT be perceived as self-citation).

All of the submissions should be submitted via EasyChair system: https://easychair.org/conferences/?conf=recsysktl2018

Please note that authors cannot be added after acceptance of papers and at least one of the paper authors is expected to attend the workshop in person to present their papers.

We are working on a special issue, and the authors will be invited to submit the extension of their work to the special issue in a journal. More information will be released later.

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