1,721,028 research outputs found
Advancing Visual Food Attractiveness Predictions for Healthy Food Recommender System
Full text linkThe visual representation of food on digital platforms affects the foods chosen by users, including in the context of recommender systems. Previous studies show that small changes in visual features can influence human decision-making, regardless of whether the food is healthy. This paper reports on a study aimed at better understanding how users perceive the attractiveness of food recipe images in the digital world. In an online mixed-methods survey (N = 192), users provided visual attractiveness ratings of food images on a 7-point scale, along with textual assessments. We found robust correlations between fundamental visual features (e.g., contrast, colorfulness) and perceived image attractiveness. The analysis also revealed that, among other user factors, cooking skills positively affected perceived image attractiveness. Regarding food image dimensions, appearance and perceived healthiness were significantly correlated with user ratings of food image attractiveness
Advancing Visual Food Attractiveness Predictions for Healthy Food Recommender System
No full textThe visual representation of food on digital platforms affects the foods chosen by users, including in the context of recommender systems. Previous studies show that small changes in visual features can influence human decision-making, regardless of whether the food is healthy. This paper reports on a study aimed at better understanding how users perceive the attractiveness of food recipe images in the digital world. In an online mixed-methods survey (N = 192), users provided visual attractiveness ratings of food images on a 7-point scale, along with textual assessments. We found robust correlations between fundamental visual features (e.g., contrast, colorfulness) and perceived image attractiveness. The analysis also revealed that, among other user factors, cooking skills positively affected perceived image attractiveness. Regarding food image dimensions, appearance and perceived healthiness were significantly correlated with user ratings of food image attractiveness
Advancing Visual Food Attractiveness Predictions for Healthy Food Recommender System
No full textThe visual representation of food on digital platforms affects the foods chosen by users, including in the context of recommender systems. Previous studies show that small changes in visual features can influence human decision-making, regardless of whether the food is healthy. This paper reports on a study aimed at better understanding how users perceive the attractiveness of food recipe images in the digital world. In an online mixed-methods survey (N = 192), users provided visual attractiveness ratings of food images on a 7-point scale, along with textual assessments. We found robust correlations between fundamental visual features (e.g., contrast, colorfulness) and perceived image attractiveness. The analysis also revealed that, among other user factors, cooking skills positively affected perceived image attractiveness. Regarding food image dimensions, appearance and perceived healthiness were significantly correlated with user ratings of food image attractiveness
Understanding How News Recommender Systems Influence Selective Exposure
Full text linkNews recommender systems (NRSs) offer benefits in the realm of information consumption and personalized news delivery. Yet, some critics argue that overly personalized news recommendations can pose a threat to democracy as these systems can potentially increase the occurrence of selective exposure, where individuals seek out political news that confirms their opinions at the expense of political news that contradicts their opinions. However, the conditions under which NRSs amplify or reduce selective exposure and the extent to which this happens are still poorly understood. Therefore, we ask: To what extent can NRSs influence the selective exposure behavior of news users We present a preregistered online experiment to empirically test the impact of structural factors on selective exposure. We track user behavior on a news website equipped with two different versions of custom-made NRSs that are specifically designed to present news articles in such a way that we assume to nudge users towards increased or decreased selective exposure to like-minded or cross-cutting news. The findings indicate that the positioning and size of news articles have a notable impact on participants behavior. Specifically, larger articles placed at the top tend to be more attractive for selection when they align with participants attitudes. On the other hand, smaller articles placed at the bottom are less likely to capture participants attention if they are attitude-consistent. The findings provide evidence that it may be possible to program NRSs to reduce selective exposure by promoting certain factors in the design of NRSs
Towards a Knowledge-aware Food Recommender System Exploiting Holistic User Models
No full textFood recommender systems typically rely on popularity, as well as similarity between recipes to generate personalized suggestions. However, this leaves little room for users to explore new preferences, such as to adopt healthier eating habits. In this short paper, we present a recommendation strategy based on knowledge about food and users' health-related characteristics to generate personalized recipes suggestions. By focusing on personal factors as a user's BMI and dietary constraints, we exploited a holistic user model to re-rank a basic recommendation list of 4,671 recipes, and investigated in a web-based experiment (N=200) to what extent it generated satisfactory food recommendations. We found that some of the information encoded in a users' holistic user profiles affected their preferences, thus providing us with interesting findings to continue this line of research
Understanding How News Recommender Systems Influence Selective Exposure
No full textNews recommender systems (NRSs) offer benefits in the realm of information consumption and personalized news delivery. Yet, some critics argue that overly personalized news recommendations can pose a threat to democracy as these systems can potentially increase the occurrence of selective exposure, where individuals seek out political news that confirms their opinions at the expense of political news that contradicts their opinions. However, the conditions under which NRSs amplify or reduce selective exposure and the extent to which this happens are still poorly understood. Therefore, we ask: To what extent can NRSs influence the selective exposure behavior of news users We present a preregistered online experiment to empirically test the impact of structural factors on selective exposure. We track user behavior on a news website equipped with two different versions of custom-made NRSs that are specifically designed to present news articles in such a way that we assume to nudge users towards increased or decreased selective exposure to like-minded or cross-cutting news. The findings indicate that the positioning and size of news articles have a notable impact on participants behavior. Specifically, larger articles placed at the top tend to be more attractive for selection when they align with participants attitudes. On the other hand, smaller articles placed at the bottom are less likely to capture participants attention if they are attitude-consistent. The findings provide evidence that it may be possible to program NRSs to reduce selective exposure by promoting certain factors in the design of NRSs
Understanding How News Recommender Systems Influence Selective Exposure
Full text linkNews recommender systems (NRSs) offer benefits in the realm of information consumption and personalized news delivery. Yet, some critics argue that overly personalized news recommendations can pose a threat to democracy as these systems can potentially increase the occurrence of selective exposure, where individuals seek out political news that confirms their opinions at the expense of political news that contradicts their opinions. However, the conditions under which NRSs amplify or reduce selective exposure and the extent to which this happens are still poorly understood. Therefore, we ask: To what extent can NRSs influence the selective exposure behavior of news users We present a preregistered online experiment to empirically test the impact of structural factors on selective exposure. We track user behavior on a news website equipped with two different versions of custom-made NRSs that are specifically designed to present news articles in such a way that we assume to nudge users towards increased or decreased selective exposure to like-minded or cross-cutting news. The findings indicate that the positioning and size of news articles have a notable impact on participants behavior. Specifically, larger articles placed at the top tend to be more attractive for selection when they align with participants attitudes. On the other hand, smaller articles placed at the bottom are less likely to capture participants attention if they are attitude-consistent. The findings provide evidence that it may be possible to program NRSs to reduce selective exposure by promoting certain factors in the design of NRSs
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Evaluating Group Recommender Systems
No full textIn the previous chapters, we have learned how to design group recommender systems but did not explicitly discuss how to evaluate them. The evaluation techniques for group recommender systems are often the same or similar to those that are used for single user recommenders. We show how to apply these techniques on the basis of examples and introduce evaluation approaches that are specifically useful in group recommendation scenarios
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