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RecSysKTL 2017 - 2017 International Workshop on Intelligent Recommender Systems by Knowledge Transfer and Learning (RecSysKTL)



VenueComo, Italy Italy



Topics/Call fo Papers

2017 International Workshop on Intelligent Recommender Systems by Knowledge Transfer and Learning (RecSysKTL)
held in conjunction with ACM Conference on Recommender Systems, Como, Italy, Aug 27, 2017
Important Dates:
June 22th, Paper Submission
July 13th, Acceptance Notification
July 27th, Camera-Ready Submission
August 27th, Workshop Date
Recommender systems, as one of well-known Web intelligence applications, aim to alleviate the information overload problem and produce item suggestions tailored to user preferences. Typically, user preferences or tastes are collected through users’ implicit or explicit feedback in various formats, such as user ratings, online behaviors, text reviews, etc. Also, user feedback on different items can be collected from several systems or domains. The diversity of feedback formats and domains provides multiple views to users’ preferences, and thus, can be helpful in recommending more related items to users. Cross-domain recommender systems and transfer learning approaches propose to 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 1st workshop on intelligent recommender systems by knowledge transfer and learning (RecSysKTL) held in conjunction with the 11th ACM Conference on Recommender Systems. 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. This workshop will be beneficial for both researchers in academia and data scientists in industry to explore and discuss different definition of domains, interesting applications, novel predictive models or recommendation approaches to serve the knowledge transfer and learning from one domain to another.
The definition of “domain” may vary in different applications, e.g., it 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
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 neworks.
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.
The topics of interest 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, while the authors will hold the copyrights. All of the submissions should be submitted via EasyChair system:
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.
Workshop Chairs
Yong Zheng, Illinois Institute of Technology, USA
Weike Pan, Shenzhen University, China
Shaghayegh (Sherry) Sahebi, University at Albany, SUNY, USA
Ignacio Fernández, NTENT, Barcelona, Spain

Last modified: 2017-04-11 23:07:15