Linked open data to support content-based recommender systems

Noia, Tommaso Di; Mirizzi, Roberto; Ostuni, Vito Claudio; Romito, Davide; Zanker, Markus

doi:10.1145/2362499.2362501

Cited by 195 publications

(114 citation statements)

References 20 publications

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“…Research in the the area of content-based recommendation systems have shown that incorporating semantic information is valuable for the user and leads to measurable improvements (Passant, 2010;Di Noia et al, 2012;Heitmann and Hayes, 2010). De Clercq et al (2014) incorporated semantic frames from FrameNet into the recommendation system for books.…”

Section: Related Workmentioning

confidence: 99%

Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources

Flek

Gurevych

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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This study focuses on personality prediction of protagonists in novels based on the Five-Factor Model of personality. We present and publish a novel collaboratively built dataset of fictional character personality and design our task as a text classification problem. We incorporate a range of semantic features, including WordNet and VerbNet sense-level information and word vector representations. We evaluate three machine learning models based on the speech, actions and predicatives of the main characters, and show that especially the lexical-semantic features significantly outperform the baselines. The most predictive features correspond to reported findings in personality psychology.

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Section: Related Workmentioning

confidence: 99%

Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources

Flek

Gurevych

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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“…The recommendation algorithm is based on the one proposed in [12], enhanced with micro-profiles management. For the sake of completeness we briefly report here the main elements of the approach.…”

Section: Content-based Recommendermentioning

confidence: 99%

“…In VSM non-binary weights are assigned to index terms in queries and in documents (represented as sets of terms), and are used to compute the degree of similarity between each document in a collection and the query. In [12] the VSM, usually used for text-based retrieval, is adapted in order to to deal with RDF graphs. In a nutshell, the whole RDF graph is represented as a 3-dimensional matrix where each slice refers to an ontology property and represents its adjacency matrix.…”

Section: Content-based Recommendermentioning

confidence: 99%

“…They define the Linked Data Semantic Distance in order to find semantic distances between resources and then compute recommendations. In [11,12] a model-based approach and a memory-based one to compute CB recommendations are presented leveraging LOD datasets. Several strategies are described and compared to select ontological properties (in the movie domain) to be used during the computation of recommended items.…”

Section: Semantics In Recommender Systemsmentioning

confidence: 99%

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Mobile Movie Recommendations with Linked Data

Ostuni

Gentile

Noia

et al. 2013

Availability, Reliability, and Security in Information Systems and HCI

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Abstract. The recent spread of the so called Web of Data has made available a vast amount of interconnected data, paving the way to a new generation of ubiquitous applications able to exploit the information encoded in it. In this paper we present Cinemappy, a location-based application that computes contextual movie recommendations. Cinemappy refines the recommendation results of a content-based recommender system by exploiting contextual information related to the current spatial and temporal position of the user. The content-based engine leverages graph information within DBpedia, one of the best-known datasets publicly available in the Linked Open Data (LOD) project.

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“…LOD facilitates the acquisition of structured data, suitable to be used for reducing the information scarcity problem in RS. In works [12,13,14], LOD has been consumed mainly for data enrichment in order to improve the performance of the recommender systems. However, given a recommendation scenario (user set, items set and the interactions between them), the way in which this data can be linked with the items and users has to be defined.…”

Section: Introductionmentioning

confidence: 99%

Linked Data-based Conceptual Modelling for Recommendation: A FCA-Based Approach

Castellanos

Garcı́a-Serrano

Cigarrán

2014

Lecture Notes in Business Information Processing

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Abstract. In a recommendation task it is crucial to have an accurate contentbased description of the users and the items consumed by them. Linked Open Data (LOD) has been demonstrated as one of the best ways of obtaining this kind of content, given its huge amount of structured information. The main question is to know how useful the LOD information is in inferring user preferences and how to obtain it. In this context, we propose a novel approach for Content Modelling and Recommendation based on Formal Concept Analysis (FCA). The approach is based in the modelling of the user and content related information, enriched with Linked Open Data, and in a new algorithm, to analyze the models and recommend new content. The framework provided by the ESWC 2014 Recommendation Challenge is used for the evaluation of the proposal. The results are within the average range of other participants, so the suitability of FCA for this scenario seems to be addressed. Nevertheless, further work has to be carried out in order to propose a refined approach for the management of LOD information.

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Linked open data to support content-based recommender systems

Cited by 195 publications

References 20 publications

Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources

Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources

Mobile Movie Recommendations with Linked Data

Linked Data-based Conceptual Modelling for Recommendation: A FCA-Based Approach

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