In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules such as “IF the entrance
Nest
security camera detects a movement, THEN blink the
Philips Hue
lamp in the kitchen.” Unfortunately, the spread of new supported technologies makes the number of possible combinations between triggers and actions continuously growing, thus motivating the need of assisting users in discovering new rules and functionality, e.g., through recommendation techniques. To this end, we present
, a semantic Conversational Search and Recommendation (CSR) system able to suggest pertinent IF-THEN rules that can be easily deployed in different contexts starting from an abstract user’s need. By exploiting a conversational agent, the user can communicate her current personalization intention by specifying a set of functionality at a high level, e.g., to decrease the temperature of a room when she left it. Stemming from this input,
implements a semantic recommendation process that takes into account (
a
) the current user’s
intention
, (
b
) the connected entities owned by the user, and (
c
) the user’s
long-term preferences
revealed by her profile. If not satisfied with the suggestions, then the user can converse with the system to provide further feedback, i.e., a
short-term preference
, thus allowing
to provide refined recommendations that better align with the original intention. We evaluate
by running different offline experiments with simulated users and real-world data. First, we test the recommendation process in different configurations, and we show that recommendation accuracy and similarity with target items increase as the interaction between the algorithm and the user proceeds. Then, we compare
with other similar baseline recommender systems. Results are promising and demonstrate the effectiveness of
in recommending IF-THEN rules that satisfy the current personalization intention of the user.
Optimization
