Noemi Mauro

• PhD in Computer Science
• Assistant Professor at University of Turin

Cultural Heritage websites’ capability to satisfy diverse information needs is limited by their high-quality but constrained knowledge bases. Thus, we investigate their extension with external large language models (LLMs), enriching the provision of cultural content by leveraging LLMs’ continuous collection and integration of information from heter...

In this report, we summarise the program and takeaways from the 1 st edition of the Workshop on Information Retrieval for Understudied Users (IR4U2), co-located with the 46 th European Conference on Information Retrieval (ECIR). Date : 24 March 2024. Website : https://ir4u2workshop.wixsite.com/ir4u2.

Information Retrieval (IR) remains an active, fast-paced area of research. However, most advances in IR have predominantly benefited the so-called “classical” users, e.g., English-speaking adults. We envision IR4U2as a forum to spotlight efforts that, while sparse, consider diverse, and often understudied, user groups when designing, developing, as...

The fashion industry accounts for a relevant portion of the environmental impact of EU consumption. Moreover, the expansion of fast fashion raises further concerns about the well-being of the people and animals involved in its production. Increasing the purchase of green clothes (i.e., sustainable garments that have been produced by brands conformi...

Field trips enrich learning programs with out-of-school activities that can bring gains in students’ academic content knowledge and personal growth. However, they are a source of anxiety for teachers because of the bureaucracy, pedagogy, etc., risks they imply. To address this issue, we propose FieldTripOrganizer, a field trip planner based on the...

People have different interests and cognitive capabilities that should be taken into account when developing technological support for cultural heritage exploration. In this project, we aim to help people with autism to plan a tourist trip by taking into account their interests and their cognitive skills. We plan to personalize the suggestion of to...

Autism is characterized by peculiar sensory processing. The sensory features of a place may have a crucial impact on the decision a person with autism makes when choosing what to visit in a tourist experience. We present a map-based mobile app, conceived for people with mid to high-functioning autism, which exploits sensory features of places to fi...

With the increasing demand for predictable and accountable Artificial Intelligence, the ability to explain or justify recommender systems results by specifying how items are suggested, or why they are relevant, has become a primary goal. However, current models do not explicitly represent the services and actors that the user might encounter during...

With the increasing demand for predictable and accountable Artificial Intelligence, the ability to explain or justify recommender systems results by specifying how items are suggested, or why they are relevant, has become a primary goal. However, current models do not explicitly represent the services and actors that the user might encounter during...

The richness of tangible and intangible cultural heritage (CH) poses great opportunities and challenges in the development of successful information and communications technology (ICT) tools for its curation, exploration and fruition [...]

When suggesting Points of Interest (PoIs) to people with autism spectrum disorders, we must take into account that they have idiosyncratic sensory aversions to noise, brightness and other features that influence the way they perceive places. Therefore, recommender systems must deal with these aspects. However, the retrieval of sensory data about Po...

When suggesting Points of Interest (PoIs) to people with autism spectrum disorders, we must take into account that they have idiosyncratic sensory aversions to noise, brightness and other features that influence the way they perceive places. Therefore, recommender systems must deal with these aspects. However, the retrieval of sensory data about Po...

The suggestion of Points of Interest (PoIs) to people with autism spectrum disorders challenges the research about recommender systems by introducing an explicit need to consider both user preferences and aversions in item evaluation. The reason is that autistic users' perception of places is influenced by sensory aversions, which can cause stress...

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 of...

In the selection of products or services, overviewing the list of options to identify the most promising ones is key to decision-making. However, current models for the justification of recommender systems results poorly support this task because, as they exclusively focus on item properties, they generate detailed justifications that are lengthy t...

The PIUMA app aims at allowing users with autism to explore their city, finding new places that can be interesting for them but at the same time do not annoy them. Users can navigate the city through an interactive map that is both personalized and crowdsourced.

Web GIS offer precious data to explore geographic areas but they might overload the user with large amounts of information if (s)he is unable to specify efficient search queries. Services such as OpenStreetMap and Google Maps support focused information search, which requires people to exactly define what they are looking for. However, what can be...

Recommender systems are designed to help users in situations of information overload. In recent years we observed increased interest in session-based recommendation scenarios, where the problem is to make item suggestions to users based only on interactions observed in an ongoing session, e.g., on an e-commerce site. However, in cases where interac...

Recommender systems are tools that support online users by pointing them to potential items of interest in situations of information overload. In recent years, the class of session-based recommendation algorithms received more attention in the research literature. These algorithms base their recommendations solely on the observed interactions with...

In online review sites, the analysis of user feedback for assessing its helpfulness for decision-making is usually carried out by locally studying the properties of individual reviews. However, global properties should be considered as well to precisely evaluate the quality of user feedback. In this paper we investigate the role of deviations in th...

In online review sites, the analysis of user feedback for assessing its helpfulness for decision-making is usually carried out by locally studying the properties of individual reviews. However, global properties should be considered as well to precisely evaluate the quality of user feedback. In this paper we investigate the role of deviations in th...

Recommender systems are designed to help users in situations of information overload. In recent years, we observed increased interest in session-based recommendation scenarios, where the problem is to make item suggestions to users based only on interactions observed in an ongoing session. However, in cases where interactions from previous user ses...

Current information exploration models present low-level features or technical aspects related to the paradigm used to generate results. While this may increase transparency, it does not help the user form a personal opinion about items because it does not describe the overall experience with them. In order to address this issue, we propose the INT...

Cultural Heritage exploration is interesting for the development of inclusive tourist guides because it exposes visitors to different types of challenges, from steering content recommendation to visitors’ interests and cognitive capabilities, to the suggestion of places that can be effectively reached and visited under different types of constraint...

This paper proposes a faceted information exploration model that supports coarse-grained and fine-grained focusing of geographic maps by offering a graphical representation of data attributes within interactive widgets. The proposed approach enables (i) a multi-category projection of long-lasting geographic maps, based on the proposal of efficient...

This paper proposes a faceted information exploration model that supports coarse-grained and fine-grained focusing of geographic maps by offering a graphical representation of data attributes within interactive widgets. The proposed approach enables (i) a multi-category projection of long-lasting geographic maps, based on the proposal of efficient...

The suggestion of Points of Interest to people with Autism Spectrum Disorder (ASD) challenges recommender systems research because these users' perception of places is influenced by idiosyncratic sensory aversions which can mine their experience by causing stress and anxiety. Therefore, managing individual preferences is not enough to provide these...

The Information Retrieval research has used semantics to provide accurate search results, but the analysis of conceptual abstraction has mainly focused on information integration. We consider session-based query expansion in Geographical Information Retrieval, and investigate the impact of semantic granularity (i.e., specificity of concepts represe...

In Geographical Information search, map visualization can challenge the user because results can consist of a large set of heterogeneous items, increasing visual complexity. We propose a novel visualization model to address this issue. Our model represents results as markers, or as geometric objects, on 2D/3D layers, using stylized and highly color...

Collaborative Filtering is largely applied to personalize item recommendation but its performance is affected by the sparsity of rating data. In order to address this issue, recent systems have been developed to improve recommendation by extracting latent factors from the rating matrices, or by exploiting trust relations established among users in...

Textual queries are largely employed in information retrieval to let users specify search goals in a natural way. However, differences in user and system terminologies can challenge the identification of the user's information needs, and thus the generation of relevant results. We argue that the explicit management of ontological knowledge, and of...

Many collaborative recommender systems leverage social correlation theories to improve suggestion performance. However, they focus on explicit relations between users and they leave out other types of information that can contribute to determine users' global reputation; e.g., public recognition of reviewers' quality. We are interested in understan...

Exploratory information search can challenge users in the formulation of efficacious search queries. Moreover, complex information spaces, such as those managed by Geographical Information Systems, can disorient people, making it difficult to find relevant data. In order to address these issues, we developed a session-based suggestion model that pr...

The crowdsourcing paradigm applied to the urban environment (i.e., people while moving can provide data from different places) may play a fundamental role in transforming users in significant actors of the places in which they live. In the last years, several crowdsourcing services have been developed to allow citizens to collaborate, by collecting...

Recommender systems are tools that support online users by pointing them to potential items of interest in situations of information overload. In recent years, the class of session-based recommendation algorithms received more attention in the research literature. These algorithms base their recommendations solely on the observed interactions with...

The benefits of neural approaches are undisputed in many application areas. However, today's research practice in applied machine learning---where researchers often use a variety of baselines, datasets, and evaluation procedures---can make it difficult to understand how much progress is actually achieved through novel technical approaches. In this...

Trust-based recommender systems improve rating prediction with respect to Collaborative Filtering by leveraging the additional information provided by a trust network among users to deal with the cold start problem. However, they are challenged by recent studies according to which people generally perceive the usage of data about social relations a...

Trust-based recommender systems improve rating prediction with respect to Collaborative Filtering by leveraging the additional information provided by a trust network among users to deal with the cold start problem. However, they are challenged by recent studies according to which people generally perceive the usage of data about social relations a...

Many collaborative recommender systems leverage social correlation theories to improve suggestion performance. However, they focus on explicit relations between users and they leave out other types of information that can contribute to determine users' global reputation; e.g., public recognition of reviewers' quality. We are interested in understa...

Collaborative Filtering is largely applied to personalize item recommendation but its performance is affected by the sparsity of rating data. In order to address this issue, recent systems have been developed to improve recommendation by extracting latent factors from the rating matrices, or by exploiting trust relations established among users in...

Thematic maps, traditionally developed to present specific themes within defined geographical areas, are an interesting information presentation model for Cultural Heritage exploration because of the abstract view on the territory they provide. However, in order to cope with possibly heterogeneous user interests, they should be adapted to the indiv...

It is our great pleasure to welcome you to the ACM 2019 PATCH. Following the successful series of PATCH workshops, started in 2007, PATCH 2019 is organized as the meeting point between state of the art cultural heritage research and personalization - using any kind of technology, while focusing on ubiquitous and adaptive scenarios, to enhance the p...

In Geographical Information search, map visualization can challenge the user because results can consist of a large set of heterogeneous items, increasing visual complexity. We propose a novel visualization model to address this issue. Our model represents results as markers, or as geometric objects, on 2D/3D layers, using stylized and highly color...

The presentation of search results in GIS can expose the user to cluttered geographical maps, challenging the identification of relevant information. In order to address this issue, we propose a visualization model supporting interactive information filtering on 2D/3D maps. Our model is based on the introduction of transparency sliders that enable...

Exploratory information search can challenge users in the formulation of efficacious search queries. Moreover, complex information spaces, such as those managed by Geographical Information Systems, can disorient people, making it difficult to find relevant data. In order to address these issues, we developed a session-based suggestion model that pr...

My PhD project focuses on the suggestion of information categories in exploratory search in a geographical domain. Geographical maps may challenge the user in the exploration of possibly complex information spaces, making difficult to find all the relevant data for the completion of her/his search task. I propose different models for concepts sugge...

Textual queries are largely employed in information retrieval to let users specify search goals in a natural way. However, differences in user and system terminologies can challenge the identification of the user's information needs, and thus the generation of relevant results. We argue that the explicit management of ontological knowledge, and of...

This demo paper describes the semantic query interpretation model adopted in the OnToMap Participatory GIS and presents its benefits to information retrieval and personalized information presentation.

The exploration of cultural heritage information is challenged by the fact that most data provided by online resources is fragmented and it is repository or application-centered. In order to address this issue, a data integration approach should be adopted, that makes it possible to generate custom views, focused on the user's information needs, bu...

We present the information retrieval model adopted in the OnToMap Participatory GIS. The model addresses the limitations of keyword-based and category-based search by semantically interpreting the information needs specified in free-text search queries. The model is based on an ontological representation of linguistic and encyclopaedic knowledge, w...

Map-based applications are a good starting point for helping teachers in the preparation of learning material and students in their researches in social sciences. However, they offer basic information filtering support to the generation of dynamic maps. In this paper, we investigate the adoption of semantic knowledge representation and cooperative...

My PhD project focuses on Participatory GIS (PGIS). In the project I analyze two methodologies to offer personalized search results in community maps and a natural interaction with the system. The first consists of automatically gathering the terms according to which the users express their information needs, in order to enrich the domain conceptua...

With the convergence of geographical information systems (GIS) and internet technology, the public administration is starting to use online maps as a web-based bidirectional communication channel with the population: maps are used: i) in public portals, for publishing and crowdsourcing information about the territory; ii) in policy-making, for defi...

Searching information in a Geographical Information System (GIS) usually imposes that users explore precompiled category catalogs and select the types of information they are looking for. Unfortunately, that approach is challenging because it forces people to adhere to a conceptualization of the information space that might be different from their...