Call for Papers

Fifth Workshop on Recommendation in Complex Scenarios

Description & Objectives

During the past decade, recommender systems have rapidly become an indispensable element of websites, apps, and other platforms that seek to provide personalized interactions to their users. As recommendation technologies are applied to an ever-growing array of non-standard problems and scenarios, researchers and practitioners are also increasingly faced with challenges of dealing with greater variety and complexity in the inputs to those recommender systems. For example, there has been more reliance on fine-grained user signals as inputs rather than simple ratings or likes. Applications require more complex domain-specific constraints on inputs to the recommender systems. Likewise, the outputs of recommender systems are moving towards more complex composite items, such as package or sequence recommendations. This increasing complexity requires smarter recommender algorithms that can deal with this diversity in inputs and outputs.

For the past four years, the ComplexRec workshop series has offered an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution. For the fifth edition of ComplexRec we have narrowed the focus of the workshop and contributions to the workshop about topics related to one of the two main themes on complex recommendation: complex inputs and complex outputs. 

 

Complex inputs

An important source of complexity comes from the various types of inputs to the system beyond users and items, such as features, queries and constraints. There are active user inputs (interaction), implicit user inputs (task, context, preferences), item inputs (features or attributes) and domain inputs (eligibility, availability). In group-based recommendation, the user input can be a combination of inputs for multiple individual users as well as group aspects such as the composition of the group and how well they know each other. An additional challenge is providing users with ways to have control over the inputs. For instance by selecting and weighting or ranking user and item features, providing interactive queries to steer the recommendations, or dealing with longer narrative statements that require natural language understanding. 

 

Complex outputs

Another type of complexity that we wish to spotlight in ComplexRec 2021 is the complexity of the outputs of a recommender system–moving away from  straightforward ranked list of items. An example of such complex output is a package recommendation: suggesting a set or combination of items that go well together and are complementary on dimensions that matter to the user. In many domains the sequence in which items are recommended is also important. Moreover, different users may want different information about items, so the output complexity goes beyond ranking; it also manifests itself in how the interface should allow the user to view the type of information that is most relevant to them. Another example of complexity in recommender systems output are scenarios where the system’s goal is to create new, composite items that must satisfy certain constraints (such as menu recommendation or recommendations for product designs).

 

Topics of interest

We plan to invite contributions that address the challenges associated with constructing recommender systems that must handle complex inputs and/or outputs. The topics of interest for the workshop include, but are not limited to:

  • Recommenders with novel complex inputs
  • Recommenders with interesting combinations of inputs
  • Recommendation with side information
  • Constraint-driven recommender systems 
  • Novel knowledge-based recommender systems
  • Novel modes of user interactions with complex inputs
  • Query-driven and interactive recommender systems
  • Query-driven, interactive, and conversational recommender systems
  • Recommending complex items, such as packages and sequences
  • Recommending composite items with complex feature interactions
  • Algorithms and models that effectively integrate complex inputs
  • Algorithms for generating personalized items based on feature preferences
  • Novel NLP approaches for dealing with complex inputs and outputs

 

Submissions

We encourage authors to submit research papers or position papers of 4-8 pages in length (excluding references) dedicated to any aspect of recommendation in complex environments.

Accepted submissions will then be invited for short presentations. Evaluation criteria for acceptance will include novelty, diversity, significance for theory/practice, quality of presentation, and the potential for sparking interesting discussion at the workshop. All submitted papers will be reviewed by the Program Committee. At least one author of each accepted paper must attend the workshop.

All submissions should be in English and should not have been published or submitted for publication elsewhere. Papers should be formatted in the ACM Proceedings Style and submitted via EasyChair (https://easychair.org/conferences/?conf=complexrec2021). Submissions will be published in the workshop proceedings.

Similar to the main RecSys conference, all authors should submit manuscripts for review in a single-column format. Instructions for Word and LaTeX authors are given below:

 

Organizers

  • Himan Abdollahpouri (himan.abdollahpouri@northwestern.edu), Northwestern University, USA
  • Toine Bogers (toine@hum.aau.dk), Aalborg University Copenhagen, Denmark
  • Bamshad Mobasher (mobasher@cs.depaul.edu), DePaul University, USA
  • Casper Petersen (cap@sampension.dk), Sampension, Denmark
  • Sole Pera (solepera@boisestate.edu), Boise State University, USA

For further questions, please contact a member of the organizing committee.

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