This volume contains the papers presented at the RecSys 2017 workshop Recommendation in Complex Scenarios (ComplexRec 2017) held on August 31, 2017 at the Villa Erba, in Como, Italy.
State-of-the-art recommenation algorithms are typically applied in relatively straightforward and static scenarios: given information about a user's past item preferences in isolation, can we predict whether they will like a new item or rank all unseen items based on predicted interest? In reality, recommendation is often a more complex problem: the evaluation of a list of recommended items never takes place in a vacuum, and it is often a single step in the user's more complex background task or need. These background needs can often place a variety of constraints on which recommendations are interesting to the user and when they are appropriate. However, relatively little research has been done on how to elicit rich information about these complex background needs or how to incorporate it into the recommendation process. Furthermore, while state-of-the-art algorithms typically work with user preferences aggregated at the item level, real users may prefer some of an item's features more than others or attach more weight in general to certain features. Finally, providing accurate and appropriate recommendations in such complex scenarios comes with a whole new set of evaluation and validation challenges.
The current generation of recommender systems and algorithms are good at addressing straightforward recommendation scenarios, but the more complex scenarios as described above have been underserved. The ComplexRec 2017 workshop aims to address this by providing an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size- ts-all solution.
The workshop program contains a set of position and research papers covering many complex aspects of recommendation in various scenarios. There were 7 submissions. Each submission was reviewed by at least 3 program committee members. The committee decided to accept 5 papers (acceptance rate 71%). The program also includes an invited keynote talk by Dietmar Jannach (Technische Universitat Dortmund).
We thank the program committee members for their timely and constructive reviews. We gratefully acknowledge the support of EasyChair for organizing paper submission and reviewing and producing the proceedings.