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Encyclopedia of Social Network Analysis and Mining pp 2064–2080Cite as

Recommender Systems Based on Linked Open Data

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Synonyms

DBpedia; Hybrid recommender systems; Information filtering; Semantic Web; Web of data

Glossary

RSs:

Recommender Systems

LOD:

Linked Open Data

Semantic Web:

Web of linked data

RDF:

Resource Description Framework

SPARQL:

Query language for the Semantic Web

SPrank:

Semantic Path-based ranking algorithm for recommendation

Definition

The Linked Open Data initiative (Bizer et al. 2009) has allowed the publication of a vast amount of data in the Semantic Web. Concurrently, growing massive amount of information on the web has led us in the Information Overload era, where the enormous amount of information and choices undermines the user experience. Recommender Systems help users to find what is relevant for them in a vast range of possibilities. Recommender Systems can benefit from the use of knowledge encoded in the Linked Open Data to provide better recommendations.

Introduction

In the last years, we assisted to the shift of the Web from a distributed collection of hyper-linked...

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References

  • Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749

    Article  Google Scholar 

  • Anand SS, Kearney P, Shapcott M (2007) Generating semantically enriched user profiles for web personalization. ACM Trans Internet Technol 7(4):22

    Article  Google Scholar 

  • Ayala VAA, Przyjaciel-Zablocki M, Hornung T, Schätzle A, Lausen G (2014) Extending sparql for recommendations. In: Proceedings of semantic web information management on semantic web information management, SWIM’14, ACM, New York, 2014, pp 1:1–1:8

    Google Scholar 

  • Balabanović M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Commun ACM 40(3):66–72

    Article  Google Scholar 

  • Bizer C, Heath T, Berners-Lee T (2009) Linked data – the story so far. Int J Semantic Web Inf Syst 5(3):1–22

    Article  Google Scholar 

  • Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data, SIGMOD ‘08, ACM, New York, 2008, pp 1247–1250

    Google Scholar 

  • Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the fourteenth conference on uncertainty in artificial intelligence, UAI’98. Morgan Kaufmann, San Francisco, pp 43–52

    Google Scholar 

  • Burke RD (2007) Hybrid web recommender systems. In: The adaptive web, methods and strategies of web personalization. Springer, Berlin/Heidelberg, pp 377–408

    Google Scholar 

  • Cantador I, Bellog´ın A, Castells P (2008) A multilayer ontology-based hybrid recommendation model. AI Commun Special Issue on Rec Sys 21(2–3):203–210

    MathSciNet  MATH  Google Scholar 

  • Cantador I, Brusilovsky P, Kuflik T (2011) Second workshop on information heterogeneity and fusion in recommender systems (hetrec 2011). In: Proceedings of the 5th ACM conference on recommender systems, RecSys 2011, ACM, New York, 2011

    Google Scholar 

  • Castells P, Hurley NJ, Vargas S (2015) Novelty and diversity in recommender systems. Springer, Boston, pp 881–918

    Chapter  Google Scholar 

  • Ceccarelli D, Lucchese C, Orlando S, Perego R, Trani S (2013) Dexter: an open source framework for entity linking. In: Proceedings of the sixth international workshop on exploiting semantic annotations in information retrieval, ESAIR ‘13, ACM, New York, 2013, pp 17–20

    Google Scholar 

  • Di Noia T, Mirizzi R, Ostuni VC, Romito D, Zanker M (2012) Linked open data to support content-based recommender systems. In: Proceedings of the 8th international conference on semantic systems, I-SEMANTICS ‘12, ACM, New York, 2012, pp 1–8

    Google Scholar 

  • Di Noia T, Ostuni VC, Tomeo P, Di Sciascio E (2016) Sprank: semantic path-based ranking for top-n recommendations using linked open data. ACM Trans Intell Syst Technol (TIST) 8(1):9:1–9:34

    Article  Google Scholar 

  • Di Noia T, Rosati J, Tomeo P, Di Sciascio E (2017) Adaptive multi-attribute diversity for recommender systems. Inform Sci 382–383:234–253

    Google Scholar 

  • Dojchinovski M, Vitvar T (2014) Personalised access to linked data. EKAW 8876:121–136

    Google Scholar 

  • Fernández-Tob´ ıas I, Cantador I, Kaminskas M, Ricci F (2011) A generic semanticbased framework for cross-domain recommendation. In: Proceedings of the 2nd international workshop on information heterogeneity and fusion in recommender systems, HetRec ‘11, ACM, New York, 2011, pp 25–32

    Google Scholar 

  • Ferragina P, Scaiella, U (2010) Tagme: on-the-fly annotation of short text fragments (by wikipedia entities). In: Proceedings of the 19th ACM international conference on information and knowledge management, CIKM ‘10, ACM, New York, 2010, pp 1625–1628

    Google Scholar 

  • de Gemmis M, Lops P, Musto C, Narducci F, Semeraro G (2015) Semantics-aware content-based recommender systems. Springer, Boston, pp 119–159

    Google Scholar 

  • Gunawardana A, Shani G (2015) Evaluating recommender systems. Springer, Boston, pp 265–308

    Chapter  Google Scholar 

  • Heitmann B, Hayes C (2010) Using linked data to build open, collaborative recommender systems. In: AAAI spring symposium: linked data meets artificial intelligence. AAAI Press, Stanford

    Google Scholar 

  • Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems and the next-generation web. In: Recommender systems. Cambridge University press, Cambridge Books Online, New York, pp 253–288

    Google Scholar 

  • Jin R, Chai JY, Si L (2004) An automatic weighting scheme for collaborative filtering. In: Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR ‘04, ACM, New York, 2004, pp 337–344

    Google Scholar 

  • Khrouf H, Troncy R (2013) Hybrid event recommendation using linked data and user diversity. In: Proceedings of the 7th ACM conference on recommender systems, RecSys ‘13, ACM, New York, 2013, pp 185–192

    Google Scholar 

  • Lehmann J, Isele R, Jakob M, Jentzsch A, Kontokostas D, Mendes PN, Hellmann S, Morsey M, van Kleef P, Auer S, Bizer C (2015) DBpedia – a large-scale, multilingual knowledge base extracted from wikipedia. Semant Web J 6(2):167–195

    Google Scholar 

  • Liu T-Y (2009) Learning to rank for information retrieval. Found Trends Inf Retr 3(3):225–331

    Article  Google Scholar 

  • Lommatzsch A, Plumbaum T, Albayrak S (2011) A linked dataverse knows better: boosting recommendation quality using semantic knowledge. In: Proceedings of the 5th International Conference on advances in semantic processing, IARIA, Wilmington, 2011, pp 97–103

    Google Scholar 

  • Maidel V, Shoval P, Shapira B, Taieb-Maimon M (2008). Evaluation of an ontologycontent based filtering method for a personalized newspaper. In: Proceedings of the 2008 ACM conference on recommender systems, RecSys 2008, Lausanne, 23–25 Oct 2008. ACM, New York, pp 91–98

    Google Scholar 

  • Marie N, Corby O, Gandon F, Ribière M (2013) Composite interests’ exploration thanks to on-the-fly linked data spreading activation. In: Proceedings of the 24th ACM conference on hypertext and social media, HT ‘13. ACM, New York, pp 31–40

    Google Scholar 

  • Mendes PN, Jakob M, A. Garc´ıa-Silva, Bizer, C (2011) Dbpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th international conference on semantic systems, I-semantics ‘11, ACM, New York, 2011, pp 1–8

    Google Scholar 

  • Middleton SE, Shadbolt NR, De Roure DC (2004) Ontological user profiling in recommender systems. ACM Trans Inf Syst 22:54–88

    Article  Google Scholar 

  • Mobasher, B, Jin X, Zhou Y (2004) Semantically enhanced collaborative filtering on the web. In: Berendt B, Hotho A, Mladenic D, Someren M, Spiliopoulou M, Stumme G (eds) Web mining: from web to semantic web, vol 3209 of lecture notes in computer science. Springer, Berlin/Heidelberg, pp 57–76

    MATH  Google Scholar 

  • Moro A, Raganato A, Navigli R (2014) Entity linking meets word sense disambiguation : a unified approach. Trans Assoc Comput Linguist 2:231–244

    Google Scholar 

  • Musto C, Semeraro G, Lops P, de Gemmis M (2014) Combining distributional semantics and entity linking for context-aware content-based recommendation. In: User modeling, adaptation, and personalization – 22nd international conference, UMAP, Aalborg, 7–11 July 2014, pp 381–392

    Google Scholar 

  • Musto C, Lops P, Basile P, de Gemmis M, Semeraro G (2016) Semantics-aware graph-based recommender systems exploiting linked open data. In: Proceedings of the 2016 conference on user modeling adaptation and personalization, UMAP ‘16, ACM, New York, 2016, pp 229–237

    Google Scholar 

  • Nguyen P, Tomeo P, Di Noia T, Di Sciascio E (2015a) An evaluation of simrank and personalized pagerank to build a recommender system for the web of data. In: Proceedings of the 24th international conference on World Wide Web, WWW ‘15 companion, ACM, New York, 2015, pp 1477–1482

    Google Scholar 

  • Nguyen PT, Tomeo P, Di Noia T, Di Sciascio, E (2015b) Content-based recommendations via DBpedia and Freebase: a case study in the music domain. Springer International Publishing, Cham, pp 605–621

    Chapter  Google Scholar 

  • Ostuni, VC, Di Noia, T, Di Sciascio, E, Mirizzi R (2013) Top-n recommendations from implicit feedback leveraging linked open data. In: Proceedings of the 7th ACM conference on recommender systems, RecSys ‘13, ACM, New York, 2013, pp 85–92

    Google Scholar 

  • Ostuni VC, Oramas S, Di Noia T, Serra X, Di Sciascio E (2016) Sound and music recommendation with knowledge graphs. ACM Trans Intell Syst Technol, 8(2):21:1–21:21

    Google Scholar 

  • Passant A (2010) Measuring semantic distance on linking data and using it for resources recommendations. In: AAAI spring symposium: linked data meets artificial intelligence. AAAI, 2010

    Google Scholar 

  • Pazzani M, Billsus D (1997) Learning and revising user profiles: the identification ofinteresting web sites. Mach Learn 27(3):313–331

    Article  Google Scholar 

  • Pazzani MJ, Billsus D (2007) Content-based recommendation systems. In: The adaptive web. Springer, Berlin/Heidelberg, pp 325–341

    Chapter  Google Scholar 

  • Ragone A, Tomeo P, Magarelli C, Di Noia T, Palmonari M, Maurino A, Di Sciascio E (2017) Schema-summarization in linked-data-based feature selection for recommender systems. In: 32nd ACM SIGAPP symposium on applied computing. ACM, New York

    Google Scholar 

  • Ricci F, Rokach L, Shapira B, eds (2015) Recommender systems handbook. Springer, New York

    Google Scholar 

  • Rizzo G, Troncy R (2012) Nerd: a framework for unifying named entity recognition and disambiguation extraction tools. In: Proceedings of the demonstrations at the 13th conference of the European chapter of the Association for Computational Linguistics, EACL ‘12, Association for Computational Linguistics, Stroudsburg, 2012, pp 73–76

    Google Scholar 

  • Rowe M (2014a) Semanticsvd++: incorporating semantic taste evolution for predicting ratings. In: 2014 IEEE/WIC/ACM international conferences on web intelligence, Wi 2014. IEEE Computer Society, Washington, DC

    Google Scholar 

  • Rowe M (2014b) Transferring semantic categories with vertex kernels: recommendations with semanticsvd++. In: The semantic web – ISWC 2014 – 13th international semantic web conference, 2014. Springer, New York

    Google Scholar 

  • Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, WWW ‘01, 2001, pp 285–295

    Google Scholar 

  • Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. In: The adaptive web. Springer, Berlin/Heidelberg, pp 291–324

    Chapter  Google Scholar 

  • Schwartz B (2005). The paradox of choice: why more is less. Harper Perennial, New York City

    Google Scholar 

  • Semeraro G, Lops P, Basile P, de Gemmis M (2009). Knowledge infusion into content-based recommender systems. In: Proceedings of the third ACM conference on recommender systems, RecSys ‘09, ACM, New York, 2009, pp 301–304

    Google Scholar 

  • Shen W, Wang J, Luo P, Wang M (2012) Linden: linking named entities with knowledge base via semantic knowledge. In: Proceedings of the 21st international conference on World Wide Web, WWW ‘12, ACM, New York, 2012, pp 449–458

    Google Scholar 

  • Speier C, Valacich JS, Vessey I (1999) The influence of task interruption on individual decision making: an information overload perspective. Decis Sci 30(2):337–360

    Article  Google Scholar 

  • Tomeo P, Fernández-Tob´ ıas I, Di Noia T, Cantador I (2016) Exploiting linked open data in cold-start recommendations with positive-only feedback. In: Proceedings of the 4th Spanish conference on information retrieval, CERI ‘16, ACM, New York, 2016, pp 11:1–11:8

    Google Scholar 

  • Ziegler C-N, Lausen G, Schmidt-Thieme L (2004) Taxonomy-driven computation of product recommendations. In: Proceedings of the thirteenth ACM international conference on information and knowledge management, CIKM ‘04, ACM, New York, 2004, pp 406–415

    Google Scholar 

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Author information

Authors and Affiliations

  1. SisInf Lab, Polytechnic University of Bari, Via Orabona 4, 70125, Bari, Italy

    Tommaso Di Noia & Paolo Tomeo

Authors
  1. Tommaso Di Noia
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  2. Paolo Tomeo
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Corresponding author

Correspondence to Tommaso Di Noia .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

  2. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Jon Rokne

Section Editor information

  1. Department of Computer Science, University of Bari "Aldo Moro", Bari, Italy

    Giovanni Semeraro

  2. Bari, Italy

    Cataldo Musto Ph.D

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Di Noia, T., Tomeo, P. (2018). Recommender Systems Based on Linked Open Data. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_110165

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