863 research outputs found
Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application
Recommender systems (RSs) are systems that produce individualized recommendations as
output or drive the user in a personalized way to interesting or useful objects in a space of possible
options. Recently, RSs emerged as an effective support for decision making. However, when people make
decisions, they usually take into account different and often conicting information such as preferences,
long-term goals, context, and their current condition. This complexity is often ignored by RSs. In order to
provide an effective decision-making support, a RS should be ``holistic'', i.e., it should rely on a complete
representation of the user, encoding heterogeneous user features (such as personal interests, psychological
traits, health data, social connections) that may come from multiple data sources. However, to obtain such
holistic recommendations some steps are necessary: rst, we need to identify the goal of the decision-making
process; then, we have to exploit common-sense and domain knowledge to provide the user with the most
suitable suggestions that best t the recommendation scenario. In this article, we present a methodological
framework that can drive researchers and developers during the design process of this kind of ``holistic'' RS.
We also provide evidence of the framework validity by presenting the design process and the evaluation of
a food RS based on holistic principles
Exploring the effects of natural language justifications in food recommender systems
Users of food recommender systems typically prefer popular recipes, which tend to be unhealthy. To encourage users to select healthier recommendations by making more informed food decisions, we introduce a methodology to generate and present a natural language justification that emphasizes the nutritional content, or health risks and benefits of recommended recipes. We designed a framework that takes a user and two food recommendations as input and produces an automatically generated natural language justification as output, which is based on the userâs characteristics and the recipesâ features. In doing so, we implemented and evaluated eight different justification strategies through two different justification styles (e.g., comparing each recipeâs food features) in an online user study (N = 503). We compared user food choices for two personalized recommendation approaches, popularity-based vs our health-aware algorithm, and evaluated the impact of presenting natural language justifications. We showed that comparative justifications styles are effective in supporting choices for our healthy-aware recommendations, confirming the impact of our methodology on food choices
Exploring the effects of natural language justifications in food recommender systems
Users of food recommender systems typically prefer popular recipes, which tend to be unhealthy. To encourage users to select healthier recommendations by making more informed food decisions, we introduce a methodology to generate and present a natural language justification that emphasizes the nutritional content, or health risks and benefits of recommended recipes. We designed a framework that takes a user and two food recommendations as input and produces an automatically generated natural language justification as output, which is based on the user's characteristics and the recipes' features. In doing so, we implemented and evaluated eight different justification strategies through two different justification styles (e.g., comparing each recipe's food features) in an online user study (N = 503). We compared user food choices for two personalized recommendation approaches, popularity-based vs our health-aware algorithm, and evaluated the impact of presenting natural language justifications. We showed that comparative justifications styles are effective in supporting choices for our healthy-aware recommendations, confirming the impact of our methodology on food choices
The interplay between food knowledge, nudges, and preference elicitation methods determines the evaluation of a recipe recommender system
Domain knowledge can affect how a user evaluates different aspects of a recommender system. Recipe recommendations might be difficult to understand, as some health aspects are implicit. The appropriateness of a recommenderâs preference elicitation (PE) method, whether users rate individual items or item attributes, may depend on the userâs knowledge level. We present an online recipe recommender experiment. Users (𝑁=360) with varying levels of subjective food knowledge faced different cognitive digital nudges (i.e., food labels) and PE methods. In a 3 (recipes annotated with no labels, Multiple Traffic Light (MTL) labels, or full nutrition labels) x2 (PE method : content-based PE or knowledge-based) between-subjects design. We observed a main effect of knowledge-based PE on the healthiness of chosen recipes, while MTL label only helped marginally. A Structural Equation Model analysis revealed that the interplay between user knowledge and the PE method reduced the perceived effort of using the system and in turn, affected choice difficulty and satisfaction. Moreover, the evaluation of health labels depends on a userâs level of food knowledge. Our findings emphasize the importance of user characteristics in the evaluation of food recommenders and the merit of interface and inter action aspects
Justification of Recommender Systems Results: A Service-based Approach
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 overall interaction with an
item, from its selection to its usage. Thus, they cannot assess their impact on
the user's experience. To address this issue, we propose a novel justification
approach that uses service models to (i) extract experience data from reviews
concerning all the stages of interaction with items, at different granularity
levels, and (ii) organize the justification of recommendations around those
stages. In a user study, we compared our approach with baselines reflecting the
state of the art in the justification of recommender systems results. The
participants evaluated the Perceived User Awareness Support provided by our
service-based justification models higher than the one offered by the
baselines. Moreover, our models received higher Interface Adequacy and
Satisfaction evaluations by users having different levels of Curiosity or low
Need for Cognition (NfC). Differently, high NfC participants preferred a direct
inspection of item reviews. These findings encourage the adoption of service
models to justify recommender systems results but suggest the investigation of
personalization strategies to suit diverse interaction needs
User Modeling and User Profiling: A Comprehensive Survey
The integration of artificial intelligence (AI) into daily life, particularly
through information retrieval and recommender systems, has necessitated
advanced user modeling and profiling techniques to deliver personalized
experiences. These techniques aim to construct accurate user representations
based on the rich amounts of data generated through interactions with these
systems. This paper presents a comprehensive survey of the current state,
evolution, and future directions of user modeling and profiling research. We
provide a historical overview, tracing the development from early stereotype
models to the latest deep learning techniques, and propose a novel taxonomy
that encompasses all active topics in this research area, including recent
trends. Our survey highlights the paradigm shifts towards more sophisticated
user profiling methods, emphasizing implicit data collection, multi-behavior
modeling, and the integration of graph data structures. We also address the
critical need for privacy-preserving techniques and the push towards
explainability and fairness in user modeling approaches. By examining the
definitions of core terminology, we aim to clarify ambiguities and foster a
clearer understanding of the field by proposing two novel encyclopedic
definitions of the main terms. Furthermore, we explore the application of user
modeling in various domains, such as fake news detection, cybersecurity, and
personalized education. This survey serves as a comprehensive resource for
researchers and practitioners, offering insights into the evolution of user
modeling and profiling and guiding the development of more personalized,
ethical, and effective AI systems.Comment: 71 page
Examining the Effects of Personalized Explanations in a Multi-list Food Recommender System
In the past decade, food recipe websites have become a popular approach to find a recipe. Due to the vast amount of options, food recommender systems have been devel- oped and used to suggest appetizing recipes. However, recommending appealing meals does not necessarily imply that they are healthy. Recent studies on recommender sys- tems have demonstrated a growing interest in altering the interface, where the usage of multi-list interfaces with explanations has been explored earlier in an unsuccessful at- tempt to encourage healthier food choices. Building upon other research that highlights the ability of personalized explanations to provide a better understanding of presented recommendations, this thesis explores whether a multi-list interface with personalized explanations, which takes into account user preferences, health, and nutritional aspects, can affect usersâ evaluation and perception of a food recommender system, as well as steer them towards healthier choices. A food recommender system was develop, with which single- and multi-lists, as well as non-personalized and personalized explana- tions, were compared in an online experiment (N = 163) in which participants were requested to choose recipes they liked and to answer questionnaires. The analysis re- vealed that personalized explanations in a multi-list interface were not able to increase choice satisfaction, choice difficulty, understanding or support healthier choices. Sur- prisingly, users selected healthier recipes if non-personalized rather than personalized explanations were presented alongside them. In addition, users perceived multi-lists to be more diverse and found single-list to be more satisfying.Masteroppgave i informasjonsvitenskapINFO390MASV-INF
Multi-list Food Recommender Systems for Healthier Choices
Recipe websites are a popular destination for home cooks to discover new recipes and find what to cook. However, the most popular way of recommending recipes to users is trough similarity and popularity-based recommendations, which previous research has shown tend to be unhealthy. Building upon knowledge on how diverse sets of options increases satisfaction, this thesis investigates whether a multi-list recommender interface can support healthier food choices compared to traditional single-list interfaces, as well as increase choice satisfaction. As diverse set of options may introduce choice overload to users, explanations were investigated in terms of how they affect user evaluation with regards to choice difficulty, perceived diversity and understandability. A developed recommender system was used in a online study (N = 366), where users could select recipes from recommendations, as well as answering short questionnaires regarding their choices. The analysis showed that a multi-list recommender system was not able to support healthier food choices. However, users who interacted with the multi-list interface found it more satisfactory compared to single-list users. No significant evidence was found that explanations could mitigate choice difficulty. This thesis provides novel work on the utilization of multi-list recommender systems with explanations in the food recommender domain, which can further be expanded with considering other factors such including personalized recommendations in the multi-list interface.Masteroppgave i informasjonsvitenskapINFO390MASV-INF
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