88 research outputs found
Group recommendation with automatic detection and classification of groups
This PhD thesis presents ART (Automatic Recommendation Technologies), a set of group recommendation algorithms that detect groups of users with similar preferences. With respect to classic group recommendation, the first step that such systems have to compute is the detection of groups of people with similar preferences, in order to respect the constraint on the number of recommendations that can be produced and maximize users’ satisfaction
Detection of Trending Topic Communities: Bridging Content Creators and Distributors
The rise of a trending topic on Twitter or Facebook leads to the temporal
emergence of a set of users currently interested in that topic. Given the
temporary nature of the links between these users, being able to dynamically
identify communities of users related to this trending topic would allow for a
rapid spread of information. Indeed, individual users inside a community might
receive recommendations of content generated by the other users, or the
community as a whole could receive group recommendations, with new content
related to that trending topic. In this paper, we tackle this challenge, by
identifying coherent topic-dependent user groups, linking those who generate
the content (creators) and those who spread this content, e.g., by
retweeting/reposting it (distributors). This is a novel problem on
group-to-group interactions in the context of recommender systems. Analysis on
real-world Twitter data compare our proposal with a baseline approach that
considers the retweeting activity, and validate it with standard metrics.
Results show the effectiveness of our approach to identify communities
interested in a topic where each includes content creators and content
distributors, facilitating users' interactions and the spread of new
information.Comment: 9 pages, 4 figures, 2 tables, Hypertext 2017 conferenc
Looks Can Be Deceiving: Linking User-Item Interactions and User's Propensity Towards Multi-Objective Recommendations
Multi-objective recommender systems (MORS) provide suggestions to users
according to multiple (and possibly conflicting) goals. When a system optimizes
its results at the individual-user level, it tailors them on a user's
propensity towards the different objectives. Hence, the capability to
understand users' fine-grained needs towards each goal is crucial. In this
paper, we present the results of a user study in which we monitored the way
users interacted with recommended items, as well as their self-proclaimed
propensities towards relevance, novelty and diversity objectives. The study was
divided into several sessions, where users evaluated recommendation lists
originating from a relevance-only single-objective baseline as well as MORS. We
show that despite MORS-based recommendations attracted less selections, its
presence in the early sessions is crucial for users' satisfaction in the later
stages. Surprisingly, the self-proclaimed willingness of users to interact with
novel and diverse items is not always reflected in the recommendations they
accept. Post-study questionnaires provide insights on how to deal with this
matter, suggesting that MORS-based results should be accompanied by elements
that allow users to understand the recommendations, so as to facilitate their
acceptance.Comment: Accepted as a short paper at ACM RecSys 2023 conference. See
https://doi.org/10.1145/3604915.360884
Interplay between upsampling and regularization for provider fairness in recommender systems
Considering the impact of recommendations on item providers is one of the duties of multi-sided recommender systems. Item providers are key stakeholders in online platforms, and their earnings and plans are influenced by the exposure their items receive in recommended lists. Prior work showed that certain minority groups of providers, characterized by a common sensitive attribute (e.g., gender or race), are being disproportionately affected by indirect and unintentional discrimination. Our study in this paper handles a situation where (i) the same provider is associated with multiple items of a list suggested to a user, (ii) an item is created by more than one provider jointly, and (iii) predicted user–item relevance scores are biasedly estimated for items of provider groups. Under this scenario, we assess disparities in relevance, visibility, and exposure, by simulating diverse representations of the minority group in the catalog and the interactions. Based on emerged unfair outcomes, we devise a treatment that combines observation upsampling and loss regularization, while learning user–item relevance scores. Experiments on real-world data demonstrate that our treatment leads to lower disparate relevance. The resulting recommended lists show fairer visibility and exposure, higher minority item coverage, and negligible loss in recommendation utility
Bias characterization, assessment, and mitigation in location-based recommender systems
Location-Based Social Networks stimulated the rise of services such as Location-based Recommender Systems. These systems suggest to users points of interest (or venues) to visit when they arrive in a specific city or region. These recommendations impact various stakeholders in society, like the users who receive the recommendations and venue owners. Hence, if a recommender generates biased or polarized results, this affects in tangible ways both the experience of the users and the providers’ activities. In this paper, we focus on four forms of polarization, namely venue popularity, category popularity, venue exposure, and geographical distance. We characterize them on different families of recommendation algorithms when using a realistic (temporal-aware) offline evaluation methodology while assessing their existence. Besides, we propose two automatic approaches to mitigate those biases. Experimental results on real-world data show that these approaches are able to jointly improve the recommendation effectiveness, while alleviating these multiple polarizationsOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This
work has been funded by the Ministerio de Ciencia e InnovaciĂłn (reference PID2019-108965GB-I00) and
by the European Social Fund (ESF), within the 2017 call for predoctoral contract
Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations
Online education platforms play an increasingly important role in mediating the success of individuals’ careers. Therefore, while building overlying content recommendation services, it becomes essential to guarantee that learners are provided with equal recommended learning opportunities, according to the platform principles, context, and pedagogy. Though the importance of ensuring equality of learning opportunities has been well investigated in traditional institutions, how this equality can be operationalized in online learning ecosystems through recommender systems is still under-explored. In this paper, we shape a blueprint of the decisions and processes to be considered in the context of equality of recommended learning opportunities, based on principles that need to be empirically-validated (no evaluation with live learners has been performed). To this end, we first provide a formalization of educational principles that model recommendations’ learning properties, and a novel fairness metric that combines them to monitor the equality of recommended learning opportunities among learners. Then, we envision a scenario wherein an educational platform should be arranged in such a way that the generated recommendations meet each principle to a certain degree for all learners, constrained to their individual preferences. Under this view, we explore the learning opportunities provided by recommender systems in a course platform, uncovering systematic inequalities. To reduce this effect, we propose a novel post-processing approach that balances personalization and equality of recommended opportunities. Experiments show that our approach leads to higher equality, with a negligible loss in personalization. This paper provides a theoretical foundation for future studies of learners’ preferences and limits concerning the equality of recommended learning opportunities
Faithful Path Language Modelling for Explainable Recommendation over Knowledge Graph
Path reasoning methods over knowledge graphs have gained popularity for their
potential to improve transparency in recommender systems. However, the
resulting models still rely on pre-trained knowledge graph embeddings, fail to
fully exploit the interdependence between entities and relations in the KG for
recommendation, and may generate inaccurate explanations. In this paper, we
introduce PEARLM, a novel approach that efficiently captures user behaviour and
product-side knowledge through language modelling. With our approach, knowledge
graph embeddings are directly learned from paths over the KG by the language
model, which also unifies entities and relations in the same optimisation
space. Constraints on the sequence decoding additionally guarantee path
faithfulness with respect to the KG. Experiments on two datasets show the
effectiveness of our approach compared to state-of-the-art baselines. Source
code and datasets: AVAILABLE AFTER GETTING ACCEPTED
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