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Recommender Systems: Sources of Knowledge and Evaluation Metrics
, 2013
"... Recommender or Recommendation Systems (RS) aim to help users dealing with information overload: finding relevant items in a vast space of resources. Research on RS has been active since the development of the first recommender sys-tem in the early 1990s, Tapestry, and some articles and books that ..."
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Cited by 5 (3 self)
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Recommender or Recommendation Systems (RS) aim to help users dealing with information overload: finding relevant items in a vast space of resources. Research on RS has been active since the development of the first recommender sys-tem in the early 1990s, Tapestry, and some articles and books that survey algorithms and application domains have been published recently. However, these surveys have not extensively covered the different types of information used in RS (sources of knowledge), and only a few of them have reviewed the different ways to assess the quality and performance of RS. In order to bridge this gap, in this chapter we present a classification of recommender systems, and then we focus on presenting the main sources of knowledge and evaluation metrics that have been described in the research literature.
Spectral Clustering for Link Prediction in Social Networks with Positive and Negative Links
"... Online social networks (OSNs) recommend new friends to registered users based on local features of the graph (i.e. based on the number of common friends that two users share). Real OSNs (e.g. Facebook) do not exploit all network structure. Instead, they consider only pathways of maximum length 2 be ..."
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Cited by 1 (0 self)
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Online social networks (OSNs) recommend new friends to registered users based on local features of the graph (i.e. based on the number of common friends that two users share). Real OSNs (e.g. Facebook) do not exploit all network structure. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. This can limit the accuracy of prediction. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we provide friend recommendations, by performing multi-way spectral clustering, which uses information obtained from the top few eigenvectors and eigenvalues of the normalized Laplacian matrix and computes a multi-way partition of the data. As a result, it produces a less noisy matrix, which is smaller and more compact than the original one, focusing on main linking trends of the social network. Thus, we are able to provide fast and more accurate friend recommendations. Moreover, spectral clustering compared to traditional clustering algorithms,
INTELLIGENT AGENT FOR INFORMATION EXTRACTION BASED ON PATTERN DISCOVERY AND ONTOLOGY
"... In recent years, several approaches have been proposed to extract information from web pages on the internet. In this research, a key technique focused on crawling and ontology used to discover knowledge from web. In this paper, we present intelligent crawling system that uses pattern and ontology t ..."
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In recent years, several approaches have been proposed to extract information from web pages on the internet. In this research, a key technique focused on crawling and ontology used to discover knowledge from web. In this paper, we present intelligent crawling system that uses pattern and ontology to extract particular information from WEB sites. The system developed as an efficient tool to construct researcher’s profile automatically from web pages. Moreover, some searching and indexing methods, text mining and computational linguistics for underlying this problem are exploited. We evaluated the performance of our system on an information extraction task from different real academic web sites. Experimental results show that with the extraction rules based on pattern discovery and ontology, our system achieves 84.90 % average of overall precision.
Challenges for industrial-strength Information Retrieval on Databases
"... ABSTRACT Implementing keyword search and other IR tasks on top of relational engines has become viable in practice, especially thanks to high-performance column-store technology. Supporting complex combinations of structured and unstructured search in real-world heterogeneous data spaces however re ..."
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ABSTRACT Implementing keyword search and other IR tasks on top of relational engines has become viable in practice, especially thanks to high-performance column-store technology. Supporting complex combinations of structured and unstructured search in real-world heterogeneous data spaces however requires more than "just" IR-on-DB. In this work, we walk the reader through our industrial-strength solution to this challenge and its application to a real-world scenario. By treating structured and unstructured search as first-class citizens of the same computational platform, much of the integration effort is pushed from the application level down to the data-management level. Combined with a visual design environment, this allows to model complex search engines without a need for programming.
Web Science in the SONIC Research Group
"... In this poster, we present an overview of the Web Science research conducted in the SONIC research group at Northwestern University. The angle taken by SONIC is to look at the Web from a network perspective. The research is theoretically rooted in a Multi-Theoretical, Multi-Level framework and in th ..."
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In this poster, we present an overview of the Web Science research conducted in the SONIC research group at Northwestern University. The angle taken by SONIC is to look at the Web from a network perspective. The research is theoretically rooted in a Multi-Theoretical, Multi-Level framework and in the empirical research new methods and tools are being applied to Web data analysis. Goals of the research are 1) to understand the Web, 2) to enable the Web and 3) re-conceptualize the Web.
Using Formal Concept Analysis for Ontology Maintenance in Human Resource Recruitment
"... Ontologies have been proven useful for many appli-cations by enabling semantic search and reasoning. Human resource management has recently attracted interest by researchers and practitioners seeking to exploit ontologies for improving the efficiency and ef-fectiveness of the job recruitment process ..."
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Ontologies have been proven useful for many appli-cations by enabling semantic search and reasoning. Human resource management has recently attracted interest by researchers and practitioners seeking to exploit ontologies for improving the efficiency and ef-fectiveness of the job recruitment process. However, the quality of semantic search and decision making in-timately depends on the quality of the ontology used. Most current efforts concentrate on the development of general ontologies that find wide approval by the HR community worldwide. In order to be useful for automatic matchmaking between job offers and job seekers, such high-level ontologies need to be ade-quately enriched with detailed domain-specific knowl-edge and adapted to the particular needs of individual job markets. We present an approach for enriching and adapting an existing ontology using formal con-cept analysis. 1
