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 make 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 HeyTAP², 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, HeyTAP² 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, the user can converse with the system to provide further feedback, i.e., a short-term preference, thus allowing HeyTAP² to provide refined recommendations that better align with the her original intention. We evaluate HeyTAP² 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 HeyTAP² with other similar baseline recommender systems. Results are promising and demonstrate the effectiveness of HeyTAP² in recommending IF-THEN rules that satisfy the current personalization intention of the user.

From Users' Intentions to IF-THEN Rules in the Internet of Things / Corno, Fulvio; De Russis, Luigi; Monge Roffarello, Alberto. - In: ACM TRANSACTIONS ON INFORMATION SYSTEMS. - ISSN 1046-8188. - STAMPA. - 39:4(2021), pp. 1-33. [10.1145/3447264]

From Users' Intentions to IF-THEN Rules in the Internet of Things

Corno, Fulvio;De Russis, Luigi;Monge Roffarello, Alberto
2021

Abstract

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 make 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 HeyTAP², 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, HeyTAP² 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, the user can converse with the system to provide further feedback, i.e., a short-term preference, thus allowing HeyTAP² to provide refined recommendations that better align with the her original intention. We evaluate HeyTAP² 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 HeyTAP² with other similar baseline recommender systems. Results are promising and demonstrate the effectiveness of HeyTAP² in recommending IF-THEN rules that satisfy the current personalization intention of the user.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2860780