We propose a new recommendation system for service and product bundling in the domain of telecommunication and multimedia. Using this system, users can easily generate a combined service plan that best suits their needs within a vast range of candidates. The system exploits the recent constructive preference elicitation framework, which allows us to flexibly model the exponentially large domain of bundle offers as an implicitly defined set of variables and constraints. The user preferences are modeled by a utility function estimated via coactive learning interaction, while iteratively generating high-utility recommendations through constraint optimization. In this paper, we detail the structure of our system, as well as the methodology and results of an empirical validation study which involved more than 130 participants. The system turned out to be highly usable with respect to both time and number of interactions, and its outputs were found much more satisfactory than those obtained ...
No more ready-made deals: constructive recommendation for telco service bundling / Dragone, Paolo; Pellegrini, Giovanni; Vescovi, Michele; Tentori, Katya; Passerini, Andrea. - (2018), pp. 163-171. ( 12th ACM Conference on Recommender Systems, RecSys 2018 Vancouver, British Columbia, Canada 2nd-7th October 2018) [10.1145/3240323.3240348].
No more ready-made deals: constructive recommendation for telco service bundling
Paolo Dragone;Pellegrini, Giovanni;Michele Vescovi;Katya Tentori;Andrea Passerini
2018-01-01
Abstract
We propose a new recommendation system for service and product bundling in the domain of telecommunication and multimedia. Using this system, users can easily generate a combined service plan that best suits their needs within a vast range of candidates. The system exploits the recent constructive preference elicitation framework, which allows us to flexibly model the exponentially large domain of bundle offers as an implicitly defined set of variables and constraints. The user preferences are modeled by a utility function estimated via coactive learning interaction, while iteratively generating high-utility recommendations through constraint optimization. In this paper, we detail the structure of our system, as well as the methodology and results of an empirical validation study which involved more than 130 participants. The system turned out to be highly usable with respect to both time and number of interactions, and its outputs were found much more satisfactory than those obtained ...| File | Dimensione | Formato | |
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