38 research outputs found
Fairness of ChatGPT and the Role Of Explainable-Guided Prompts
Our research investigates the potential of Large-scale Language Models
(LLMs), specifically OpenAI's GPT, in credit risk assessment-a binary
classification task. Our findings suggest that LLMs, when directed by
judiciously designed prompts and supplemented with domain-specific knowledge,
can parallel the performance of traditional Machine Learning (ML) models.
Intriguingly, they achieve this with significantly less data-40 times less,
utilizing merely 20 data points compared to the ML's 800. LLMs particularly
excel in minimizing false positives and enhancing fairness, both being vital
aspects of risk analysis. While our results did not surpass those of classical
ML models, they underscore the potential of LLMs in analogous tasks, laying a
groundwork for future explorations into harnessing the capabilities of LLMs in
diverse ML tasks
Toward building a content-based video recommendation system based on low-level features
One of the challenges in video recommendation systems is the New Item problem, which happens when the system is unable to recommend video items, that no information is available about them. For example, in the popular movie-sharing websites, such as Youtube, every-day, hundred millions of hours of videos are uploaded and big portion of these videos may not contain any meta-data, to be used by the system to generate recommendations. In this paper, we address this problem by proposing a method, that is based on automatic analysis of the video content in order to extract a number representative low-level visual features. Such features are then used to generate personalized content-based recommendations. Our evaluation shows that our proposed method can outperform the baselines, by producing more relevant recommendations. Hence, a set low-level features extracted automatically can be more descriptive and informative of the video content than a set of high-level expert annotated features
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
Recommender systems fairness evaluation via generalized cross entropy
Fairness in recommender systems has been considered with respect
to sensitive attributes of users (e.g., gender, race) or items (e.g., revenue
in a multistakeholder setting). Regardless, the concept has been
commonly interpreted as some form of equality – i.e., the degree to
which the system is meeting the information needs of all its users in
an equal sense. In this paper, we argue that fairness in recommender
systems does not necessarily imply equality, but instead it should
consider a distribution of resources based on merits and needs.We
present a probabilistic framework based ongeneralized cross entropy
to evaluate fairness of recommender systems under this perspective,
wherewe showthat the proposed framework is flexible and explanatory
by allowing to incorporate domain knowledge (through an ideal
fair distribution) that can help to understand which item or user aspects
a recommendation algorithm is over- or under-representing.
Results on two real-world datasets show the merits of the proposed
evaluation framework both in terms of user and item fairnessThis work was supported in part by the Center for Intelligent Information
Retrieval and in part by project TIN2016-80630-P (MINECO
Exploring the Semantic Gap for Movie Recommendations
In the last years, there has been much attention given to the semantic gap problem in multimedia retrieval systems. Much effort has been devoted to bridge this gap by building tools for the extraction of high-level, semantics-based features from multimedia content, as low-level features are not considered useful because they deal primarily with representing the perceived content rather than the semantics of it.
In this paper, we explore a different point of view by leveraging the gap between low-level and high-level features. We experiment with a recent approach for movie recommendation that extract low-level Mise-en-Scéne features from multimedia content and combine it with high-level features provided by the wisdom of the crowd.
To this end, we first performed an offline performance assessment by implementing a pure content-based recommender system with three different versions of the same algorithm, respectively based on (i) conventional movie attributes, (ii) mise-en-scene features, and (iii) a hybrid method that interleaves recommendations based on movie attributes and mise-en-scene features. In a second study, we designed an empirical study involving 100 subjects and collected data regarding the quality perceived by the users. Results from both studies show that the introduction of mise-en-scéne features in conjunction with traditional movie attributes improves both offline and online quality of recommendations