12,437 research outputs found
Layered evaluation of multi-criteria collaborative filtering for scientific paper recommendation
Recommendation algorithms have been researched extensively to help people deal with abundance of information. In recent years, the incorporation of multiple relevance criteria has attracted increased interest. Such multi-criteria recommendation approaches are researched as a paradigm for building intelligent systems that can be tailored to multiple interest indicators of end-users – such as combinations of implicit and explicit interest indicators in the form of ratings or ratings on multiple relevance dimensions. Nevertheless, evaluation of these recommendation techniques in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined that the performance of such algorithms is often dependent on the dataset – and indicate the importance of carrying out careful testing and parameterization. Especially when looking at large scale datasets, it becomes very difficult to deploy evaluation methods that may help in assessing the effect that different system components have to the overall design. In this paper, we study how layered evaluation can be applied for the case of a multi-criteria recommendation service that we plan to deploy for paper recommendation using the Mendeley dataset. The paper introduces layered evaluation and suggests two experiments that may help assess the components of the envisaged system separately. Keywords: Recommender systems; Multi-Criteria Decision Making (MCDM); Evaluatio
Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation
The multi-criteria (MC) recommender system, which leverages MC rating
information in a wide range of e-commerce areas, is ubiquitous nowadays.
Surprisingly, although graph neural networks (GNNs) have been widely applied to
develop various recommender systems due to GNN's high expressive capability in
learning graph representations, it has been still unexplored how to design MC
recommender systems with GNNs. In light of this, we make the first attempt
towards designing a GNN-aided MC recommender system. Specifically, rather than
straightforwardly adopting existing GNN-based recommendation methods, we devise
a novel criteria preference-aware light graph convolution CPA-LGC method, which
is capable of precisely capturing the criteria preference of users as well as
the collaborative signal in complex high-order connectivities. To this end, we
first construct an MC expansion graph that transforms user--item MC ratings
into an expanded bipartite graph to potentially learn from the collaborative
signal in MC ratings. Next, to strengthen the capability of criteria preference
awareness, CPA-LGC incorporates newly characterized embeddings, including
user-specific criteria-preference embeddings and item-specific criterion
embeddings, into our graph convolution model. Through comprehensive evaluations
using four real-world datasets, we demonstrate (a) the superiority over
benchmark MC recommendation methods and benchmark recommendation methods using
GNNs with tremendous gains, (b) the effectiveness of core components in
CPA-LGC, and (c) the computational efficiency.Comment: 12 pages, 10 figures, 5 tables; 29th ACM SIGKDD Conference on
Knowledge Discovery & Data (KDD 2023) (to appear) (Please cite our conference
version.
NCBO Ontology Recommender 2.0: An Enhanced Approach for Biomedical Ontology Recommendation
Biomedical researchers use ontologies to annotate their data with ontology
terms, enabling better data integration and interoperability. However, the
number, variety and complexity of current biomedical ontologies make it
cumbersome for researchers to determine which ones to reuse for their specific
needs. To overcome this problem, in 2010 the National Center for Biomedical
Ontology (NCBO) released the Ontology Recommender, which is a service that
receives a biomedical text corpus or a list of keywords and suggests ontologies
appropriate for referencing the indicated terms. We developed a new version of
the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a new
recommendation approach that evaluates the relevance of an ontology to
biomedical text data according to four criteria: (1) the extent to which the
ontology covers the input data; (2) the acceptance of the ontology in the
biomedical community; (3) the level of detail of the ontology classes that
cover the input data; and (4) the specialization of the ontology to the domain
of the input data. Our evaluation shows that the enhanced recommender provides
higher quality suggestions than the original approach, providing better
coverage of the input data, more detailed information about their concepts,
increased specialization for the domain of the input data, and greater
acceptance and use in the community. In addition, it provides users with more
explanatory information, along with suggestions of not only individual
ontologies but also groups of ontologies. It also can be customized to fit the
needs of different scenarios. Ontology Recommender 2.0 combines the strengths
of its predecessor with a range of adjustments and new features that improve
its reliability and usefulness. Ontology Recommender 2.0 recommends over 500
biomedical ontologies from the NCBO BioPortal platform, where it is openly
available.Comment: 29 pages, 8 figures, 11 table
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