Marko Tkalcic

Colour spaces: perceptual, historical and applicational background

In this paper, we present and overview of colour spaces used in electrical engineering and image processing. We stress the importance of the perceptual, historical and applicational background that led to a colour space. The colour spaces presented are: RGB, opponent-colour spaces, phenomenal colour spaces, CMY, CMYK, TV colour spaces (YUV and YIQ), PhotoYCC, CIE XYZ, Lab and Luv colour spaces.

Personality-Based Active Learning for Collaborative Filtering Recommender Systems

Recommender systems (RSs) suffer from the cold-start or new user/item problem, i.e., the impossibility to provide a new user with accurate recommendations or to recommend new items. Active learning (AL) addresses this problem by actively selecting items to be presented to the user in order to acquire her ratings and hence improve the output of the RS. In this paper, we propose a novel AL approach that exploits the users personality - using the Five Factor Model (FFM) - in order to identify the items that the user is requested to rate. We have evaluated our approach in a user study by integrating it into a mobile, context-aware RS that provides users with recommendations for places of interest (POIs). We show that the proposed AL approach significantly increases the number of ratings acquired from the user and the recommendation accuracy.

Predicting and Detecting the Relevant Contextual Information in a Movie-Recommender System

Context-aware recommender system (CARS) is a highly researched and implemented way of providing a personalized service that helps users to find their desired content. One of the remaining issues is how to decide which contextual information to acquire and how to incorporate it into CARS. While the relevant contextual information will improve the recommendations, the irrelevant contextual information could have a negative impact on the recommendation accuracy. By testing the independence between the contextual variable on the users’ ratings for items, we can detect its relevanceandimpactonthefeedbackfortheitemconsumedinthatspecificcontext.Inthisarticle,we propose several new theoretical concepts that should help deciding which information to use, as well as a methodology for detecting which contextual information contributes to explaining the variance in the ratings, based on statistical testing. The experiment was conducted on the real movie dataset that contains 12 different pieces of contextual information. We used two statistical tests with power analysis for the detection, and three contextualized matrix-factorization algorithms with slightly different reasoning for the prediction of ratings. The results showed a significant difference in the prediction of ratings in the context that was detected as relevant by our method, and the one that was detected as irrelevant, pointing to the importance of the power analysis and the benefits of the proposed method in the case of a small dataset.

Predicting Personality Traits with Instagram Pictures

Instagram is a popular social networking application, which allows photo-sharing and applying different photo filters to adjust the appearance of a picture. By applying photo filters, users are able to create a style that they want to express to their audience. In this study we tried to infer personality traits from the way users take pictures and apply filters to them. To investigate this relationship, we conducted an online survey where we asked participants to fill in a personality questionnaire, and grant us access to their Instagram account through the Instagram API. Among 113 participants and 22,398 extracted Instagram pictures, we found distinct picture features (e.g., hue, brightness, saturation) that are related to personality traits. Our findings suggest a relationship between personality traits and the way users want to make their pictures look. This allow for new ways to extract personality traits from social media trails, and new ways to facilitate personalized systems.

Personality and Recommender Systems

Personality, as defined in psychology, accounts for the individual differences in users’ preferences and behaviour. It has been found that there are significant correlations between personality and users’ characteristics that are traditionally used by recommender systems (e.g. music preferences, social media behaviour, learning styles etc.). Among the many models of personality, the Five Factor Model (FFM) appears suitable for usage in recommender systems as it can be quantitatively measured (i.e. numerical values for each of the factors, namely, openness, conscientiousness, extraversion, agreeableness and neuroticism). The acquisition of the personality factors for an observed user can be done explicitly through questionnaires or implicitly using machine learning techniques with features extracted from social media streams or mobile phone call logs. There are, although limited, a number of available datasets to use in offline recommender systems experiment. Studies have shown that personality was successful at tackling the cold-start problem, making group recommendations, addressing cross-domain preferences and at generating diverse recommendations. However, a number of challenges still remain.

Using Instagram Picture Features to Predict Users' Personality

Instagram is a popular social networking application, which allows photo-sharing and applying different photo filters to adjust the appearance of a picture. By applying these filters, users are able to create a style that they want to express to their audience. In this study we tried to infer personality traits from the way users manipulate the appearance of their pictures by applying filters to them. To investigate this relationship, we studied the relationship between picture features and personality traits. To collect data, we conducted an online survey where we asked participants to fill in a personality questionnaire, and grant us access to their Instagram account through the Instagram API. Among 113 participants and 22,398 extracted Instagram pictures, we found distinct picture features e.g., relevant to hue, brightness, saturation that are related to personality traits. Our findings suggest a relationship between personality traits and these picture features. Based on our findings, we also show that personality traits can be accurately predicted. This allow for new ways to extract personality traits from social media trails, and new ways to facilitate personalized systems.

The LDOS-PerAff-1 corpus of facial-expression video clips with affective, personality and user-interaction metadata

We present the LDOS-PerAff-1 Corpus that bridges the affective computing and recommender system research areas, which makes it unique. The corpus is composed of video clips of subjects’ affective responses to visual stimuli. These affective responses are annotated in the continuous valence-arousal-dominance space. Furthermore, the subjects are annotated with their personality information using the five-factor personality model. We also provide the explicit ratings that the users gave to the images used for the visual stimuli. In the paper we present the results of four experiments conducted with the corpus; an affective content-based recommender system, a personality-based collaborative filtering recommender system, an emotion-detection algorithm and a qualitative study of the latent factors.

Emotion-Aware Recommender Systems – A Framework and a Case Study

Recent work has shown an increase of accuracy in recommender systems that use emotive labels. In this paper we propose a framework for emotion-aware recommender systems and present a survey of the results in such recommender systems. We present a consumption-chain-based framework and we compare three labeling methods within a recommender system for images: (i) generic labeling, (ii) explicit affective labeling and (iii) implicit affective labeling.

Genre-based Analysis of Social Media Data on Music Listening Behavior: Are Fans of Classical Music Really Averse to Social Media?

It is frequently presumed that lovers of Classical music are not present in social media. In this paper, we investigate whether this statement can be empirically verified. To this end, we compare two social media platforms --- Last.fm and Twitter --- and perform a study on musical preference of their respective users. We investigate two research hypotheses: (i) Classical music fan are more reluctant to use social media to indicate their listing habits than listeners of other genres and (ii) there are correlations between the use of Last.fm and Twitter to indicate music listening behavior. Both hypotheses are verified and substantial differences could be made out for Twitter users. The results of these investigations will help improve music recommendation systems for listeners with non-mainstream music taste.

On the Influence of User Characteristics on Music Recommendation Algorithms

We investigate a range of music recommendation algorithm combinations, score aggregation functions, normalization techniques, and late fusion techniques on approximately 200 million listening events collected through Last.fm. The overall goal is to identify superior combinations for the task of artist recommendation. Hypothesizing that user characteristics influence performance on these algorithmic combinations, we consider specific user groups determined by age, gender, country, and preferred genre. Overall, we find that the performance of music recommendation algorithms highly depends on user characteristics.