# Service-Aware Interactive Presentation of Items for Decision-Making
## 2 Background and Related Work
### Explanation of Recommender Systems Suggestions
For this purpose,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of System** & **Dimensions of Item Exploration** & **Visualization/Explanation** & **Citation** \\ \hline information exploration & search keywords/concepts & color coding of result list & [36; 37] \\ information exploration & search keywords/concepts & 2D plan-based visualization of results & [14; 39; 40] \\ information exploration & search keywords/concepts & 2D/3D visualization of clusters of results & [41; 42] \\ RS & item properties & group items by trade-off properties & [43] \\ collaborative RS & similar users/items & group user ratings, describe past performance & [44] \\ content-based RS & user content similarity & any & [45] \\ feature-based and & features utility & feature-based, bar charts & [25; 46; 47; 48; 49] \\ multicriteria RS & user-item relations & relation graph & [6; 50; 51; 52] \\ graph-based RS & supporting recommender & stackable bars, relation graphs, grids, textual explanation, & [7; 8; 9; 53; 54; 55; 56] \\ hybrid RS & supporting recommender & Venn diagrams & [57; 65; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 8  
we invest in improving the efficiency of analysis of individual items, assuming that the user has picked them, e.g., from a ranked list. In this perspective, we offer a simple, interactive model to (i) visualize a synthesis of consumer sentiment about the evaluation dimensions selected by the user and (ii) provide direct access to the reviews supporting that sentiment. In this way the user can easily identify the comments relevant to her/his interests and form a \"first-hand\" opinion about items. Our work is related to [PERSON] et al. 's one [25] because both models present item dimensions by means of charts. However, we provide holistic information about items, based on high-level service evaluation dimensions; differently, in the other work the diagrams support the visualization of low-level item properties such as music tracks' tempo and acousticness. Similar to the work in [51; 75; 76], our model is agnostic with respect to how items are filtered. However, rather than justifying recommendations by means of a synthesis of reviews, we aim at empowering the user to efficiently explore the existing feedback to make a personal opinion about items. This has a triple advantage: (i) it supports user awareness of item properties; (ii) it does not infringe the transparency of the recommender system; and (iii) it is not affected by issues related to personal characteristics, which in some domains impact on the user's confidence in the explanations (s)he receives [77]. Review-based recommender systems [57] extract item features and aspects from online user feedback to build user and item models for personalized recommendations [59; 60; 61; 62], or to build user-item chains of relations for graph-based recommendation as in TriRank [52]. While most works strictly focus on improving Top-N recommendation (see, e.g., in [58; 63; 64; 65]), [PERSON] and [PERSON] [71] introduce the idea of matching relevant item features extracted from reviews to latent factors in order to support explanation in Matrix Factorization. In the same line of research, [PERSON] et al. [66] and [PERSON] and [PERSON] [68] mine bigram and single-noun phrases and map them to items metadata to address general features in explanations. Moreover, [PERSON] and [PERSON] [69] and [PERSON] et al. [70] extract positive and negative opinions about item features to integrate recommendation and explanation of results into a single task. Furthermore, [PERSON] and [PERSON] [67] classify extracted features using metadata to group them in explanation. Differently, SearchLens [13] enables the user to define search \"lenses\" that specify the desired features of items; it returns result lists enriched with a specification of the degree of matching between items and search criteria.