33 research outputs found
Mining the social semantic Web for making cross-domain recommendations
This is an electronic version of the paper presented at the Fifth BCS-IRSG Symposium on Future Directions in Information Access, held in Granada on 2003Cross-domain recommender systems filter and suggest items in a target domain by exploiting user preferences and/or domain knowledge available in a (likely related) source domain. In our research we are developing a framework for cross-domain recommendation capable of mining heterogeneous sources of information available in the so-called Social Semantic Web, such as semantically annotated data, user generated contents, and contextual signals
On the exploitation of user personality in recommender systems
Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Â Proceedings of the First International Workshop on Decision Making and Recommender Systems (DMRS2014)In this paper we revise the state of the art on personality-aware
recommender systems, identifying main research trends and achievements up to
date, and discussing open issues that may be addressed in the future.This work was supported by the Spanish Ministry of Science and Innovation
(TIN2013-47090-C3-2)
Matrix factorization models for cross-domain recommendation : Addressing the cold start in collaborative filtering
Tesis doctoral inĂ©dita leĂda en la Universidad AutĂłnoma de Madrid, Facultad de la Escuela PolitĂ©cnica Superior, Departamento de IngenierĂa Informática. Fecha de lectura :13-01-201
cTag: Semantic Contextualisation of Social Tags
Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Â Proceedings of the Workshop on Semantic Adaptive Social Web 2011In this paper, we present an algorithmic framework to identify the
semantic meanings and contexts of social tags within a particular folksonomy,
and exploit them for building contextualised tag-based user and item profiles.
We also present its implementation in a system called cTag, with which we
preliminary analyse semantic meanings and contexts of tags belonging to
Delicious and MovieLens folksonomies.This work was supported by the Spanish Ministry of Science and Innovation
(TIN2008-06566-C04-02), and the Regional Government of Madrid (S2009TIC-
1542)
Modeling emotions with social tags
Proceedings of 21th International Conference, UMAP 2013, Rome, Italy, June 10-14, 2013The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38844-6_30We present an emotion model based on social tags, which is built upon an automatically generated lexicon that describes emotions by means of synonym and antonym terms. Using this model we develop a number of methods that transform social tag-based item profiles into emotion-oriented item profiles. We show that the model’s representation of a number of basic emotions is in accordance with the well known psychological circumplex model of affect, and we report results from a user study that show a high precision of our methods to infer the emotions evoked by items in the movie and music domains.This work was supported by the Spanish Government (TIN2011-28538-C02) and the Regional Government of Madrid (S2009TIC-1542)
Relating personality types with user preferences in multiple entertainment domains
Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Â Late-Breaking Results, Project Papers and Workshop Proceedings of the 21st Conference on User Modeling, Adaptation, and Personalization, UMAP 2013We present a preliminary study on the relations between personality
types and user preferences in multiple entertainment domains, namely movies,
TV shows, music, and books. We analyze a total of 53,226 Facebook user
profiles composed of both personality scores (openness, conscientiousness,
extraversion, agreeableness, neuroticism) from the Five Factor model, and
explicit interests about 16 genres in each of the above domains. As a result of
our analysis, we extract personality-based user stereotypes and association rules
for some of the considered domain genres, and infer similarities of personality
types related to genres in different domains.This work was supported by the Spanish Government (TIN2011-28538-C02) and the
Regional Government of Madrid (S2009TIC-1542). The authors sincerely thank the
members of myPersonality project for their kind attention and help on downloading
and processing the provided data
Parsimonious and Adaptive Contextual Information Acquisition in Recommender Systems
Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Â Context-Aware Recommender System (CARS) models are
trained on datasets of context-dependent user preferences
(ratings and context information). Since the number of
context-dependent preferences increases exponentially with
the number of contextual factors, and certain contextual in-
formation is still hard to acquire automatically (e.g., the
user's mood or for whom the user is buying the searched
item) it is fundamental to identify and acquire those factors
that truly in
uence the user preferences and the ratings. In
particular, this ensures that (i) the user e ort in specifying
contextual information is kept to a minimum, and (ii) the
system's performance is not negatively impacted by irrele-
vant contextual information. In this paper, we propose a
novel method which, unlike existing ones, directly estimates
the impact of context on rating predictions and adaptively
identi es the contextual factors that are deemed to be useful
to be elicited from the users. Our experimental evaluation
shows that it compares favourably to various state-of-the-art
context selection methods
Knowledge-based identification of music suited for places of interest
The final publication is available at Springer via http://dx.doi.org/10.1007/s40558-014-0004-xPlace is a notion closely linked with the wealth of human experience, and invested by values, attitudes, and cultural influences. In particular, many places are strongly related to music, which contributes to shaping the perception and meaning of a place. In this paper we propose a computational approach to identify musicians and music suited for a place of interest (POI)––which is based on a knowledge-based framework built upon the DBpedia ontology––and a graph-based algorithm that scores musicians with respect to their semantic relatedness with a POI and suggests the top scoring ones. Through empirical experiments we show that users appreciate and judge the musician recommendations generated by the proposed approach as valuable, and perceive compositions of the suggested musicians as suited for the POIs.This work was supported by the Spanish Government (TIN201128538C02)
and the
Regional Government of Madrid (S2009TIC1542)
Radiative thermal escape in intermediate band solar cells
To achieve high efficiency, the intermediate band (IB) solar cell must generate photocurrent from sub-bandgap photons at a voltage higher than that of a single contributing sub-bandgap photon. To achieve the latter, it is necessary that the IB levels be properly isolated from the valence and conduction bands. We prove that this is not the case for IB cells formed with the confined levels of InAs quantum dots (QDs) in GaAs grown so far due to the strong density of internal thermal photons at the transition energies involved. To counteract this, the QD must be smaller
Interaction Design in a Mobile Food Recommender System
Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Â One of the most important steps in building a recommender
system is the interaction design process, which de nes how
the recommender system interacts with a user. It also shapes
the experience the user gets, from the point she registers
and provides her preferences to the system, to the point
she receives recommendations generated by the system. A
proper interaction design may improve user experience and
hence may result in higher usability of the system, as well
as, in higher satisfaction.
In this paper, we focus on the interaction design of a mo-
bile food recommender system that, through a novel interac-
tion process, elicits users' long-term and short-term prefer-
ences for recipes. User's long-term preferences are captured
by asking the user to rate and tag familiar recipes, while for
collecting the short-term preferences, the user is asked to
select the ingredients she would like to include in the recipe
to be prepared. Based on the combined exploitation of both
types of preferences, a set of personalized recommendations
is generated. We conducted a user study measuring the us-
ability of the proposed interaction. The results of the study
show that the majority of users rates the quality of the rec-
ommendations high and the system achieves usability scores
above the standard benchmark