# Service-Aware Interactive Presentation of Items for Decision-Making
## Materials and Methods
### Review Analysis
#### 3.2.3 Binding Review Sentences to Evaluation Dimensions
Moreover, some users first analyzed the histogram to check the general trend of the evaluation dimensions along time; then, in a second step, they read the reviews to gather more precise information about the home. Conversely, some users directly read the information corresponding to the highlighted keywords in the reviews, overlooking the chart. The latter behavior can be explained by the fact that people are more familiar with the list of reviews that is typical in booking and e-commerce platforms, than with diagrams. Participants also gave some suggestions for future work. Specifically, they said that it would be useful to have a summary of the home reporting, e.g., the total number of reviews and its overall rating. In this way, users would be able to filter out the apartments that are below their own standards; for instance, users said that if a home had few reviews or a bad evaluation, they would not consider it at all. People also suggested a function to filter reviews by keyword in order to quickly find the comments dealing with specific topics. Finally, some participants felt confused about the initial impact \begin{table}
\begin{tabular}{l c c c} \hline  & **Baseline** & ** INTEREST** & **Relative Difference** \\ \hline Attractiveness & \(\rightarrow\) 40.50\% & \(\uparrow\) 81.36\% & 100.90\% \\ Perspicuity & \(\rightarrow\) 58.88\% & \(\uparrow\) 82.79\% & 40.60\% \\ Efficiency & \(\downarrow\) 35.09\% & \(\uparrow\) 85.96\% & 145.00\% \\ Dependability & \(\rightarrow\) 56.14\% & \(\uparrow\) 79.82\% & 42.19\% \\ Stimulation & \(\downarrow\) 30.81\% & \(\uparrow\) 77.74\% & 152.31\% \\
**Novelty** & \(\downarrow\) 21.27\% & \(\uparrow\) 79.61\% & 274.23\% \\ _Awareness and control_ & \(\rightarrow\) 56.73\% & \(\uparrow\) 83.33\% & 46.89\% \\ \hline \end{tabular}
\end{table} 
Table 6: Post-task questionnaire results in a [0, 100] scale with relative difference between Baseline and INTEREST on UX factors. The colored arrows have the same meaning as in Table 5.

of the set of colors used to highlight the words in the reviews. They suggested to have this functionality on demand, which is particularly interesting because it supports the idea of adapting explanations and visualization to individual needs, proposed in works such as [25]. ## 6 Discussion

The positive results of the user study show that the INTEREST visualization model underlying Apartment Monitoring efficaciously supports the selection of items within a set of search results by empowering users to analyze item properties from a service-oriented perspective. Our model supports the presentation of item data by means of a user interface which the participants of the study considered as attractive, novel, stimulating, perspicuous, and dependable. From the viewpoint of user awareness and control, participants confirmed that the system was able to describe the experience to be expected from the homes, that they were aware of the properties of the homes, and that the system supported them in their selection. Thus, people felt in control during information exploration and decision-making. We believe that participants' perception of the Apartment Monitoring application strongly depended on two key elements: first, users were empowered to directly access the reviews dealing with the evaluation dimensions they were interested in; second, data interpretation was supported by the graphical visualization of dimension values and of relevant keywords from the reviews. In fact, the focus on specific evaluation dimensions and the direct access to reviews, which are graphically annotated to highlight their cues, support the overview of the available items and a quick acquisition of a first-hand opinion about them. The high perception of the system efficiency in the post-task questionnaires and the observations of people's behavior during the experiment (either using histograms first and then focusing on the selected reviews, or directly exploiting keyword highlights in the reviews to search for relevant information) support our hypothesis.