Service-Aware Personalized Item Recommendation

Abstract

Current recommender systems employ item-centric properties to estimate ratings and present the results to the user. However, recent studies highlight the fact that the stages of item fruition also involve extrinsic factors, such as the interaction with the service provider before, during and after item selection. In other words, a holistic view on consumer experience, including local properties of items, as well as consumers' perceptions of item fruition, should be adopted to enhance user awareness and decision-making. In this work, we integrate recommender systems with service models to reason about the different stages of item fruition. By exploiting the Service Journey Maps to define service-based item and user profiles, we develop a novel family of recommender systems that evaluate items by taking preference management and overall consumer experience into account. Moreover, we introduce a two-level visual model to provide users with different information about recommendation results: (i) the higher level summarizes consumer experience about items and supports the identification of promising suggestions within a possibly long list of results; (ii) the lower level enables the exploration of detailed data about the local properties of items. In a user test instantiated in the home-booking domain, we compared our models to standard recommender systems. We found that the service-based algorithms that only use item fruition experience excel in ranking and in the minimization of the error in rating estimation. Moreover, the combination of data about item fruition experience and item properties achieves slightly lower recommendation performance; however, it enhances users' perceptions of the awareness and of the decision-making support provided by the system. These results encourage the adoption of service-based models to summarize user preferences and experience in recommender systems.

Justification of recommender systems results: a service-based approach

“…Mauro et al. ( 2022 ) tested the recommendation performance of a few service-aware recommender systems that leverage consumer feedback to extract coarse-grained experience evaluation dimensions of items. Those dimensions guide (i) the rating estimation, (ii) a visual summarization of the sentiment emerging from the reviews, and (iii) the indexing of reviews by evaluation dimension.…”

Justification of Recommender Systems Results: A Service-based Approach

“…In this context, we pursue the justification of recommender systems results because it is agnostic with respect to the applied algorithms but can be exploited to enhance users' awareness of the pros and cons of the items suggested by the recommender. Mauro et al (2022) tested the recommendation performance of a few service-aware recommender systems that leverage consumer feedback to extract coarse-grained experience evaluation dimensions of items. Those dimensions guide (i) the rating estimation, (ii) a visual summarization of the sentiment emerging from the reviews, and (iii) the indexing of reviews by evaluation dimension.…”