2,328 research outputs found
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Hybrid Recommender Systems: A Systematic Literature Review
Recommender systems are software tools used to generate and provide suggestions for items
and other entities to the users by exploiting various strategies. Hybrid recommender systems
combine two or more recommendation strategies in different ways to benefit from their complementary
advantages. This systematic literature review presents the state of the art in hybrid
recommender systems of the last decade. It is the first quantitative review work completely focused
in hybrid recommenders. We address the most relevant problems considered and present
the associated data mining and recommendation techniques used to overcome them. We also
explore the hybridization classes each hybrid recommender belongs to, the application domains,
the evaluation process and proposed future research directions. Based on our findings, most of
the studies combine collaborative filtering with another technique often in a weighted way. Also
cold-start and data sparsity are the two traditional and top problems being addressed in 23 and
22 studies each, while movies and movie datasets are still widely used by most of the authors.
As most of the studies are evaluated by comparisons with similar methods using accuracy metrics,
providing more credible and user oriented evaluations remains a typical challenge. Besides
this, newer challenges were also identified such as responding to the variation of user context,
evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid
recommenders represent a good basis with which to respond accordingly by exploring newer
opportunities such as contextualizing recommendations, involving parallel hybrid algorithms,
processing larger datasets, etc
Personalized content retrieval in context using ontological knowledge
Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. However, not all user preferences are relevant in all situations. It is well known that human preferences are complex, multiple, heterogeneous, changing, even contradictory, and should be understood in context with the user goals and tasks at hand. In this paper, we propose a method to build a dynamic representation of the semantic context of ongoing retrieval tasks, which is used to activate different subsets of user interests at runtime, in a way that out-of-context preferences are discarded. Our approach is based on an ontology-driven representation of the domain of discourse, providing enriched descriptions of the semantics involved in retrieval actions and preferences, and enabling the definition of effective means to relate preferences and context
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Local search: A guide for the information retrieval practitioner
There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR
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