End users can personalize their smart devices and web applications by defining or reusing trigger-action (IF-THEN) rules through dedicated End-User Development (EUD) tools, like IFTTT. Despite apparent simplicity, however, such tools present their own set of issues. The emerging and increasing complexity of the Internet of Things, for instance, is barely taken into account, and the number of possible combinations between triggers and actions of different smart devices and web applications is continuously growing. Such a large design space makes end-user personalization a complex task for non-programmers, and motivates the need of assisting users in easily discovering and managing rules and functionality, e.g., through recommendation techniques.
The paper RecRules: Recommending IF-THEN Rules for End-User Development, published in the ACM Transactions on Intelligent Systems and Technologies, presents RecRules, a hybrid and semantic recommendation system, as as solution for tackling the emerging problem of recommending trigger-action rules to end users. Through a mixed content and collaborative approach, RecRules recommends by functionality: it suggests rules based on their final purposes, thus overcoming details like manufacturers and brands.
The algorithm uses a semantic reasoning process to enrich rules with semantic information, with the aim of uncovering hidden connections between rules in terms of shared functionality. Then, it builds a collaborative semantic graph, and it exploits different types of path-based features to train a learning to rank algorithm and compute top-N recommendations. We evaluate RecRules through different experiments on real user data extracted from IFTTT, one of the most popular EUD tool. Results are promising: they show the effectiveness of our approach with respect to other state-of-the-art algorithms, and open the way for a new class of recommender systems for EUD that take into account the actual functionality needed by end users.
More information:
- Paper on the ACM DL
- Paper on PORTO@IRIS, our institutional repository
- DOI: 10.1145/3344211