Automatic Selection of Linked Open Data Features in Graph-based Recommender Systems (original) (raw)
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A semantic similarity measure for linked data: An information content-based approach
2016
Linked Data allows structured data to be published in a standard manner so that datasets from diverse domains can be interlinked. By leveraging Semantic Web standards and technologies, a growing amount of semantic content has been published on the Web as Linked Open Data (LOD). The LOD cloud has made available a large volume of structured data in a range of domains via liberal licenses. The semantic content of LOD in conjunction with the advanced searching and querying mechanisms provided by SPARQL has opened up unprecedented opportunities not only for enhancing existing applications, but also for developing new and innovative semantic applications. However, SPARQL is inadequate to deal with functionalities such as comparing, prioritizing, and ranking search results which are fundamental to applications such as recommendation provision, matchmaking, social network analysis, visualization, and data clustering. This paper addresses this problem by developing a systematic measurement model of semantic similarity between resources in Linked Data. By drawing extensively on a feature-based definition of Linked Data, it proposes a generalized information content-based approach that improves on previous methods which are typically restricted to specific knowledge representation models and less relevant in the context of Linked Data. It is validated and evaluated for measuring item similarity in recommender systems. The experimental evaluation of the proposed measure shows that our approach can outperform comparable recommender systems that use conventional similarity measures.
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2020
Preferring accuracy over computation time or vice versa is very challenging in the context of recommendation systems, which encourages many researchers to opt for hybrid recommendation systems. Currently, researchers are trying hard to produce correct and accurate recommendations by suggesting the use of ontology, but the lack of techniques renders to take its full advantage. One of the major issues in recommender systems bothering many researchers is pure new user cold-start problem which arises due to the absence of information in the system about the new user. Linked Open Data (LOD) initiative sets standards for interoperability among cross domains and has gathered enormous amount of data over the past years, which provides various ways by which recommender system’s performance can be improved by enriching user’s profile with relevant features. This research work focuses on solving pure new user cold-start problem by building user’s profile based on LOD, collaborative features, a...
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