1,188 research outputs found
Recommending Items in Social Tagging Systems Using Tag and Time Information
In this work we present a novel item recommendation approach that aims at
improving Collaborative Filtering (CF) in social tagging systems using the
information about tags and time. Our algorithm follows a two-step approach,
where in the first step a potentially interesting candidate item-set is found
using user-based CF and in the second step this candidate item-set is ranked
using item-based CF. Within this ranking step we integrate the information of
tag usage and time using the Base-Level Learning (BLL) equation coming from
human memory theory that is used to determine the reuse-probability of words
and tags using a power-law forgetting function.
As the results of our extensive evaluation conducted on data-sets gathered
from three social tagging systems (BibSonomy, CiteULike and MovieLens) show,
the usage of tag-based and time information via the BLL equation also helps to
improve the ranking and recommendation process of items and thus, can be used
to realize an effective item recommender that outperforms two alternative
algorithms which also exploit time and tag-based information.Comment: 6 pages, 2 tables, 9 figure
Improving argumentation-based recommender systems through context-adaptable selection criteria
Recommender Systems based on argumentation represent an important proposal where the recommendation is supported by qualitative information. In these systems, the role of the comparison criterion used to decide between competing arguments is paramount and the possibility of using the most appropriate for a given domain becomes a central issue; therefore, an argumentative recommender system that offers an interchangeable argument comparison criterion provides a significant ability that can be exploited by the user. However, in most of current recommender systems, the argument comparison criterion is either fixed, or codified within the arguments. In this work we propose a formalization of context-adaptable selection criteria that enhances the argumentative reasoning mechanism. Thus, we do not propose of a new type of recommender system; instead we present a mechanism that expand the capabilities of existing argumentation-based recommender systems. More precisely, our proposal is to provide a way of specifying how to select and use the most appropriate argument comparison criterion effecting the selection on the user´s preferences, giving the possibility of programming, by the use of conditional expressions, which argument preference criterion has to be used in each particular situation.Fil: Teze, Juan Carlos Lionel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional de Entre Ríos; ArgentinaFil: Gottifredi, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: García, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentin
Building Ethically Bounded AI
The more AI agents are deployed in scenarios with possibly unexpected
situations, the more they need to be flexible, adaptive, and creative in
achieving the goal we have given them. Thus, a certain level of freedom to
choose the best path to the goal is inherent in making AI robust and flexible
enough. At the same time, however, the pervasive deployment of AI in our life,
whether AI is autonomous or collaborating with humans, raises several ethical
challenges. AI agents should be aware and follow appropriate ethical principles
and should thus exhibit properties such as fairness or other virtues. These
ethical principles should define the boundaries of AI's freedom and creativity.
However, it is still a challenge to understand how to specify and reason with
ethical boundaries in AI agents and how to combine them appropriately with
subjective preferences and goal specifications. Some initial attempts employ
either a data-driven example-based approach for both, or a symbolic rule-based
approach for both. We envision a modular approach where any AI technique can be
used for any of these essential ingredients in decision making or decision
support systems, paired with a contextual approach to define their combination
and relative weight. In a world where neither humans nor AI systems work in
isolation, but are tightly interconnected, e.g., the Internet of Things, we
also envision a compositional approach to building ethically bounded AI, where
the ethical properties of each component can be fruitfully exploited to derive
those of the overall system. In this paper we define and motivate the notion of
ethically-bounded AI, we describe two concrete examples, and we outline some
outstanding challenges.Comment: Published at AAAI Blue Sky Track, winner of Blue Sky Awar
Bridging Systems: Open Problems for Countering Destructive Divisiveness across Ranking, Recommenders, and Governance
Divisiveness appears to be increasing in much of the world, leading to
concern about political violence and a decreasing capacity to collaboratively
address large-scale societal challenges. In this working paper we aim to
articulate an interdisciplinary research and practice area focused on what we
call bridging systems: systems which increase mutual understanding and trust
across divides, creating space for productive conflict, deliberation, or
cooperation. We give examples of bridging systems across three domains:
recommender systems on social media, collective response systems, and
human-facilitated group deliberation. We argue that these examples can be more
meaningfully understood as processes for attention-allocation (as opposed to
"content distribution" or "amplification") and develop a corresponding
framework to explore similarities - and opportunities for bridging - across
these seemingly disparate domains. We focus particularly on the potential of
bridging-based ranking to bring the benefits of offline bridging into spaces
which are already governed by algorithms. Throughout, we suggest research
directions that could improve our capacity to incorporate bridging into a world
increasingly mediated by algorithms and artificial intelligence.Comment: 40 pages, 11 figures. See https://bridging.systems for more about
this wor
Ontological Matchmaking in Recommender Systems
The electronic marketplace offers great potential for the recommendation of
supplies. In the so called recommender systems, it is crucial to apply
matchmaking strategies that faithfully satisfy the predicates specified in the
demand, and take into account as much as possible the user preferences. We
focus on real-life ontology-driven matchmaking scenarios and identify a number
of challenges, being inspired by such scenarios. A key challenge is that of
presenting the results to the users in an understandable and clear-cut fashion
in order to facilitate the analysis of the results. Indeed, such scenarios
evoke the opportunity to rank and group the results according to specific
criteria. A further challenge consists of presenting the results to the user in
an asynchronous fashion, i.e. the 'push' mode, along with the 'pull' mode, in
which the user explicitly issues a query, and displays the results. Moreover,
an important issue to consider in real-life cases is the possibility of
submitting a query to multiple providers, and collecting the various results.
We have designed and implemented an ontology-based matchmaking system that
suitably addresses the above challenges. We have conducted a comprehensive
experimental study, in order to investigate the usability of the system, the
performance and the effectiveness of the matchmaking strategies with real
ontological datasets.Comment: 28 pages, 8 figure
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