412 research outputs found
Whatâs going on in my city? Recommender systems and electronic participatory budgeting
In this paper, we present electronic participatory budgeting (ePB) as a novel application domain for recommender systems. On public data from the ePB platforms of three major US cities â Cambridge, Miami and New York Cityâ, we evaluate various methods that exploit heterogeneous sources and models of user preferences to provide personalized recommendations of citizen proposals. We show that depending on characteristics of the cities and their participatory processes, particular methods are more effective than others for each city. This result, together with open issues identified in the paper, call for further research in the area
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews
In popular applications such as e-commerce sites and social media, users
provide online reviews giving personal opinions about a wide array of items, such
as products, services and people. These reviews are usually in the form of free text,
and represent a rich source of information about the usersâ preferences. Among the
information elements that can be extracted from reviews, opinions about particular
item aspects (i.e., characteristics, attributes or components) have been shown to be
effective for user modeling and personalized recommendation. In this paper, we investigate
the aspect-based recommendation problem by separately addressing three
tasks, namely identifying references to item aspects in user reviews, classifying the
sentiment orientation of the opinions about such aspects in the reviews, and exploiting
the extracted aspect opinion information to provide enhanced recommendations. Differently
to previous work, we integrate and empirically evaluate several state-of-the-art
and novel methods for each of the above tasks. We conduct extensive experiments
on standard datasets and several domains, analyzing distinct recommendation quality
metrics and characteristics of the datasets, domains and extracted aspects. As a result
of our investigation, we not only derive conclusions about which combination of methods
is most appropriate according to the above issues, but also provide a number of
valuable resources for opinion mining and recommendation purposes, such as domain
aspect vocabularies and domain-dependent, aspect-level lexiconsThis work was supported by the Spanish Ministry of Economy, Industry and Competitiveness
(TIN2016-80630-P)
Multicriteria Evaluation for Top-k and Sequence-based Recommender Systems
L'abstract Ăš presente nell'allegato / the abstract is in the attachmen
Learner course recommendation in e-learning based on swarm intelligence
Se dan unas recomendaciones en la enseñanza asistida por ordenador (e-learning) basada en la inteligencia colectiva.This paper analyses aspects about the recommendation process in distributedinformation systems. It extracts similarities and differences between recommendations in estores and the recommendations applied to an e-learning environment. It also explains the phenomena of self-organization and cooperative emergence in complex systems coupled with bio-inspired algorithms to improve knowledge discovery and association rules. Finally, the present recommendation is applied to e-learning by proposing recommendation by emergence in a multi.agent system architecture
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