# Service-Aware Interactive Presentation of Items for Decision-Making
## Materials and Methods
### Review Analysis
#### 3.2.3 Binding Review Sentences to Evaluation Dimensions
Consequently, we revised the original evaluation dimensions and we obtained those represented in Figure 4, i.e., Host appreciation, Search on website, Check-in/Check-out, In-apartment experience, and Surroundings. Table 2 shows a few sample keywords for each thesaurus and Table 1 in the Appendix A reports the full list. We remind the reader that these (lemmatized) keywords are used to index review sentences with respect to the corresponding evaluation dimensions and to highlight review words in the user interface of Apartment Monitoring. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Evaluation Dimension** & **\#Keywords** & **Sample Lemmatized Keywords** \\ \hline Host appreciation & 10 & host, owner, renter, interaction, hospitality,... \\ Search on website & 18 & search, reservation, booking, arrangement, agreement,... \\ Check-in/Check-out & 37 & arrival, welcome, key, reception, check-in, check-out,... \\ In-apartment experience & 180 & bed, bedroom, bathroom, bath, kitchen, internet, exterior,... \\ Surroundings & 70 & beach, transport, c\#es, restaurant, shops, bus, park,... \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation dimensions and sample lemmatized keywords for the thesauri of the home booking domain. Table 1 in the Appendix A shows the complete thesauri. Figure 4: The upper portion of this figure shows the Service Journey Map related to the home booking experience. Each stage of the map is connected to the associated evaluation dimensions. Evaluation dimensions are colored as in the user interface of the Apartment Monitoring application. ### Study Design

We conducted a user study using Apartment Monitoring to test the support of the INTEREST model to item evaluation. We assume that a previous filtering phase provides the user with a list \(\mathcal{L}\) of items that satisfy her/his basic requirements (e.g., arrival and leaving dates and number of guests), and that (s)he interacts with the system to overview the properties of the filtered items, leveraging online reviews to evaluate them. Therefore, starting from a list \(\mathcal{L}\) of results, the experiment investigates the level of support provided by our application to make an informed item selection decision. As discussed in Section 2, existing information exploration and recommender systems focus on rather specific item features and aspects, or they attempt to explain the recommendation paradigm applied to generate the results. Therefore, they cannot provide the user with a holistic, service-oriented presentation of items, which is a main objective of our work. Moreover, the systems generating item overviews have rather complex user interfaces that can overload users. We thus decided to compare our approach to models that leverage review data to provide a direct impression of what consumers think about items. In our case, it was natural to consider home booking services, and Airbnb in particular. We thus analyzed the following. * The INTEREST model in its Apartment Monitoring implementation. This model empowers the user to evaluate items by means of (i) interactive charts that summarize consumer feedback, (ii) visual annotations of reviews that highlight (in synch with the charts) the evaluation dimensions of the experience, and (iii) a temporal selection of reviews. * A Baseline model that shows the textual reviews as in most booking and e-commerce platforms. To build a strong baseline, we included in this model the date picker supporting the selection of the time frame of interest for the selection of the reviews to be inspected. We asked each participant of the study to imagine that (s)he had to plan a holiday in the city of Florence and to select an apartment for renting. We explained that, using the two systems, (s)he should have analyzed the properties of the host, home, etc., based on the task to be solved, in order to select the apartment that (s)he would have liked to rent. We aimed at separately evaluating INTEREST and Baseline but we wanted to minimize the learning effect on participants.