Fourth Workshop on Recommendation in Complex Environments @ RecSys 2020, Rio de Janeiro

Description & objectives

During the past decade, recommender systems have rapidly become an indispensable element of websites, apps, and other platforms that are looking to provide personalized interaction 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. Many applications also require more complex domain-specific constraints on inputs to the recommender systems. The outputs of recommender systems are also 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 three years, the ComplexRec workshop series has offered an interactive venue for discussing approaches to recommendation in complex environments that have no simple one-size-fits-all solution. For the fourth 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 recommendation, or deal with longer narrative statements that require natural language understanding. 

Complex outputs

Another type of complexity that we wish to focus on in ComplexRec 2020 is the complexity of the outputs of a recommender system to move away from a straightforward ranked list of items as output. An example of such complex output is  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 and 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 environments 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
  • Constraint-driven recommender systems 
  • Novel knowledge-based recommender systems
  • Novel modes of user interactions with complex inputs
  • Query-driven and interactive recommender systems
  • Algorithms and models that effectively integrate complex inputs
  • Recommending complex items, such as packages and sequences
  • Recommending composite items with complex feature interactions
  • Algorithms for generating personalized items based on feature preferences
  • Novel NLP approaches for dealing with complex inputs and outputs



We encourage authors to submit short papers and 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, double-blind and should not have been published or submitted for publication elsewhere. Papers should be formatted in the ACM Proceedings Style and submitted via EasyChair ( 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:

LaTeX: Please use the latest version of the Master Article Template – LaTeX ( to create your submission. You must use the “manuscript” option with the \documentclass[manuscript]{acmart} command to generate the output in a single-column format which is required for review. Please see the LaTeX documentation ( and ACM’s LaTeX best practices guide ( for further instructions.


Important dates

See the ‘Important dates’ page for up-to-date information about the submission and acceptance dates.