Recommender Systems and Linked Open Data

Noia, Tommaso Di; Ostuni, Vito Claudio

doi:10.1007/978-3-319-21768-0_4

Cited by 43 publications

(25 citation statements)

References 56 publications

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“…For example, the latent representation of the entities could be used for building content-based recommender systems [4]. The approach could also be used for link predictions, type prediction, graph completion and error detection in knowledge graphs [19], as shown in [15,17].…”

Section: Resultsmentioning

confidence: 99%

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RDF2Vec: RDF Graph Embeddings for Data Mining

Ristoski

Paulheim

2016

Lecture Notes in Computer Science

302

266

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Abstract. Linked Open Data has been recognized as a valuable source for background information in data mining. However, most data mining tools require features in propositional form, i.e., a vector of nominal or numerical features associated with an instance, while Linked Open Data sources are graphs by nature. In this paper, we present RDF2Vec, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs. We generate sequences by leveraging local information from graph substructures, harvested by Weisfeiler-Lehman Subtree RDF Graph Kernels and graph walks, and learn latent numerical representations of entities in RDF graphs. Our evaluation shows that such vector representations outperform existing techniques for the propositionalization of RDF graphs on a variety of different predictive machine learning tasks, and that feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be easily reused for different tasks.

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Section: Resultsmentioning

confidence: 99%

“…For the Wikidata dataset we use the simplified and derived RDF dumps from 2016-03-28 4 . The dataset contains 17, 340, 659 entities in total.…”

Section: Datasetsmentioning

confidence: 99%

RDF2Vec: RDF Graph Embeddings for Data Mining

Ristoski

Paulheim

2016

Lecture Notes in Computer Science

302

266

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“…Di Noia et al [9] and [3] are one of authors that have using content-based filtering. The system allows upgrading a recommendation system with linked data and using the Vector Space Model (VSM) and apply it to the recommendation of movies.…”

Section: B Content-based Filteringmentioning

confidence: 99%

“…The availability of such data is for sure an opportunity to feed personalized information access tools such as recommender systems [3].…”

Section: Introductionmentioning

confidence: 99%

Towards Semantics-Aware Recommender System: A LOD-Based Approach

Fridi¹,

Benslimane²

2017

IJMECS

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Abstract-Recommender systems have contributed to the success of personalized websites as they can automatically and efficiently select items or services adapted to the user's interest from huge datasets. However, these systems suffer of issues related to small number of evaluations; cold start system and data sparsity. Several approaches have been explored to find solutions to related issues. The advent of the Linked Open Data (LOD) initiative has spawned a wide range of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. In this paper, we aim to demonstrate that adding semantic information from LOD enhance the effectiveness of traditional collaborative filtering. To evaluate the accuracy of the semantic approach, experiments on standard benchmark dataset was conducted. The obtained results indicate that the accuracy and quality of the recommendation are improved compared with existing approaches.

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“…Their approach recommends the items using the ontology and inferred preferences while computing similarities. A more detailed description of ontology based techniques is available in [28] and [29].…”

Section: B Ontology Based Recommender Systemmentioning

confidence: 99%

An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

Karpus

Vagliano

Goczyła

et al. 2016

Annals of Computer Science and Information Systems

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Abstract-Context-aware Recommender Systems aim to provide users with the most adequate recommendations for their current situation. However, an exact context obtained from a user could be too specific and may not have enough data for accurate rating prediction. This is known as the data sparsity problem. Moreover, often user preference representation depends on the domain or the specific recommendation approach used. Therefore, a big effort is required to change the method used. In this paper we present a new approach for contextual pre-filtering (i.e. using the current context to select a relevant subset of data). Our approach can be used with existing recommendation algorithms. It is based on two ontologies: Recommender System Context ontology, which represents the context, and Contextual Ontological User Profile ontology, which represents user preferences. We evaluated our approach through an offline study which showed that when used with well-known recommendation algorithms it can significantly improve the accuracy of prediction.

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Recommender Systems and Linked Open Data

Cited by 43 publications

References 56 publications

RDF2Vec: RDF Graph Embeddings for Data Mining

RDF2Vec: RDF Graph Embeddings for Data Mining

Towards Semantics-Aware Recommender System: A LOD-Based Approach

An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

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