�ݺ�ߣshows by User: mirizzi

�ݺ�ߣshows by User: mirizzi / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: mirizzi / Mon, 15 Jul 2013 13:16:02 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: mirizzi Recommender Systems in the Linked Data era /slideshow/recommender-systems-in-the-linked-data-era/24260185 recommendersystemsinthelinkeddataera-130715131602-phpapp01
The ultimate goal of a recommender system is to suggest interesting and not obvious items (e.g., products to buy, people to connect with, movies to watch, etc.) to users, based on their preferences. The advent of the Linked Open Data (LOD) initiative in the Semantic Web gave birth to a variety 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. Here I present several approaches to recommender systems that leverage Linked Data knowledge bases such as DBpedia. In particular, content-based and hybrid recommendation algorithms will be discussed. For full details about the presented approaches please refer to the full papers mentioned in this presentation.]]>
The ultimate goal of a recommender system is to suggest interesting and not obvious items (e.g., products to buy, people to connect with, movies to watch, etc.) to users, based on their preferences. The advent of the Linked Open Data (LOD) initiative in the Semantic Web gave birth to a variety 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. Here I present several approaches to recommender systems that leverage Linked Data knowledge bases such as DBpedia. In particular, content-based and hybrid recommendation algorithms will be discussed. For full details about the presented approaches please refer to the full papers mentioned in this presentation.]]> Mon, 15 Jul 2013 13:16:02 GMT /slideshow/recommender-systems-in-the-linked-data-era/24260185 mirizzi@slideshare.net(mirizzi) Recommender Systems in the Linked Data era mirizzi The ultimate goal of a recommender system is to suggest interesting and not obvious items (e.g., products to buy, people to connect with, movies to watch, etc.) to users, based on their preferences. The advent of the Linked Open Data (LOD) initiative in the Semantic Web gave birth to a variety 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. Here I present several approaches to recommender systems that leverage Linked Data knowledge bases such as DBpedia. In particular, content-based and hybrid recommendation algorithms will be discussed. For full details about the presented approaches please refer to the full papers mentioned in this presentation. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/recommendersystemsinthelinkeddataera-130715131602-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> The ultimate goal of a recommender system is to suggest interesting and not obvious items (e.g., products to buy, people to connect with, movies to watch, etc.) to users, based on their preferences. The advent of the Linked Open Data (LOD) initiative in the Semantic Web gave birth to a variety 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. Here I present several approaches to recommender systems that leverage Linked Data knowledge bases such as DBpedia. In particular, content-based and hybrid recommendation algorithms will be discussed. For full details about the presented approaches please refer to the full papers mentioned in this presentation.

Recommender Systems in the Linked Data era from Roku

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0 Linked Open Data to Support Content-based Recommender Systems - I-SEMANTIC… /slideshow/linked-open-data-to-support-contentbased-recommender-systems-isemantic/16183010 i-semantics2012-130125174955-phpapp01
Linked Open Data to Support Content-based Recommender Systems Tommaso Di Noia, Roberto Mirizzi, Vito Claudio Ostuni, Davide Romito, Markus Zanker I-SEMANTICS 2012, 8th Int. Conf. on Semantic Systems, Sept. 5-7, 2012, Graz, Austria. The World Wide Web is moving from a Web of hyper-linked Documents to a Web of linked Data. Thanks to the Semantic Web spread and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets. These datasets are connected with each other to form the so called Linked Open Data cloud. As of today, there are tons of RDF data available in the Web of Data, but only few applications really exploit their potential power. In this paper we show how these data can successfully be used to develop a recommender system (RS) that relies exclusively on the information encoded in the Web of Data. We implemented a content-based RS that leverages the data available within Linked Open Data datasets (in particular DBpedia, Freebase and LinkedMDB) in order to recommend movies to the end users. We extensively evaluated the approach and validated the effectiveness of the algorithms by experimentally measuring their accuracy with precision and recall metrics.]]>
Linked Open Data to Support Content-based Recommender Systems Tommaso Di Noia, Roberto Mirizzi, Vito Claudio Ostuni, Davide Romito, Markus Zanker I-SEMANTICS 2012, 8th Int. Conf. on Semantic Systems, Sept. 5-7, 2012, Graz, Austria. The World Wide Web is moving from a Web of hyper-linked Documents to a Web of linked Data. Thanks to the Semantic Web spread and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets. These datasets are connected with each other to form the so called Linked Open Data cloud. As of today, there are tons of RDF data available in the Web of Data, but only few applications really exploit their potential power. In this paper we show how these data can successfully be used to develop a recommender system (RS) that relies exclusively on the information encoded in the Web of Data. We implemented a content-based RS that leverages the data available within Linked Open Data datasets (in particular DBpedia, Freebase and LinkedMDB) in order to recommend movies to the end users. We extensively evaluated the approach and validated the effectiveness of the algorithms by experimentally measuring their accuracy with precision and recall metrics.]]> Fri, 25 Jan 2013 17:49:55 GMT /slideshow/linked-open-data-to-support-contentbased-recommender-systems-isemantic/16183010 mirizzi@slideshare.net(mirizzi) Linked Open Data to Support Content-based Recommender Systems - I-SEMANTIC… mirizzi Linked Open Data to Support Content-based Recommender Systems Tommaso Di Noia, Roberto Mirizzi, Vito Claudio Ostuni, Davide Romito, Markus Zanker I-SEMANTICS 2012, 8th Int. Conf. on Semantic Systems, Sept. 5-7, 2012, Graz, Austria. The World Wide Web is moving from a Web of hyper-linked Documents to a Web of linked Data. Thanks to the Semantic Web spread and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets. These datasets are connected with each other to form the so called Linked Open Data cloud. As of today, there are tons of RDF data available in the Web of Data, but only few applications really exploit their potential power. In this paper we show how these data can successfully be used to develop a recommender system (RS) that relies exclusively on the information encoded in the Web of Data. We implemented a content-based RS that leverages the data available within Linked Open Data datasets (in particular DBpedia, Freebase and LinkedMDB) in order to recommend movies to the end users. We extensively evaluated the approach and validated the effectiveness of the algorithms by experimentally measuring their accuracy with precision and recall metrics. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/i-semantics2012-130125174955-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Linked Open Data to Support Content-based Recommender Systems Tommaso Di Noia, Roberto Mirizzi, Vito Claudio Ostuni, Davide Romito, Markus Zanker I-SEMANTICS 2012, 8th Int. Conf. on Semantic Systems, Sept. 5-7, 2012, Graz, Austria. The World Wide Web is moving from a Web of hyper-linked Documents to a Web of linked Data. Thanks to the Semantic Web spread and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets. These datasets are connected with each other to form the so called Linked Open Data cloud. As of today, there are tons of RDF data available in the Web of Data, but only few applications really exploit their potential power. In this paper we show how these data can successfully be used to develop a recommender system (RS) that relies exclusively on the information encoded in the Web of Data. We implemented a content-based RS that leverages the data available within Linked Open Data datasets (in particular DBpedia, Freebase and LinkedMDB) in order to recommend movies to the end users. We extensively evaluated the approach and validated the effectiveness of the algorithms by experimentally measuring their accuracy with precision and recall metrics.

Linked Open Data to Support Content-based Recommender Systems - I-SEMANTIC… from Roku

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0 Movie Recommendation with DBpedia - IIR 2012 /slideshow/movie-recommendation-with-dbpedia-iir-2012/11891071 iir2012-120306131316-phpapp02
Movie Recommendation with DBpedia Roberto Mirizzi, Tommaso Di Noia, Azzurra Ragone, Vito Claudio Ostuni, Eugenio Di Sciascio 3rd Italian Information Retrieval Workshop (IIR 2012) - Bari January 26, 2012 In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic sim- ilarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. Precision and recall experiments prove the validity of our ap- proach for movie recommendation. MORE is freely available as a Facebook application. ]]>
Movie Recommendation with DBpedia Roberto Mirizzi, Tommaso Di Noia, Azzurra Ragone, Vito Claudio Ostuni, Eugenio Di Sciascio 3rd Italian Information Retrieval Workshop (IIR 2012) - Bari January 26, 2012 In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic sim- ilarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. Precision and recall experiments prove the validity of our ap- proach for movie recommendation. MORE is freely available as a Facebook application. ]]> Tue, 06 Mar 2012 13:13:13 GMT /slideshow/movie-recommendation-with-dbpedia-iir-2012/11891071 mirizzi@slideshare.net(mirizzi) Movie Recommendation with DBpedia - IIR 2012 mirizzi Movie Recommendation with DBpedia Roberto Mirizzi, Tommaso Di Noia, Azzurra Ragone, Vito Claudio Ostuni, Eugenio Di Sciascio 3rd Italian Information Retrieval Workshop (IIR 2012) - Bari January 26, 2012 In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic sim- ilarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. Precision and recall experiments prove the validity of our ap- proach for movie recommendation. MORE is freely available as a Facebook application. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iir2012-120306131316-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Movie Recommendation with DBpedia Roberto Mirizzi, Tommaso Di Noia, Azzurra Ragone, Vito Claudio Ostuni, Eugenio Di Sciascio 3rd Italian Information Retrieval Workshop (IIR 2012) - Bari January 26, 2012 In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic sim- ilarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. Precision and recall experiments prove the validity of our ap- proach for movie recommendation. MORE is freely available as a Facebook application.

Movie Recommendation with DBpedia - IIR 2012 from Roku

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0 From Exploratory Search to Web Search and back - PIKM 2010 /slideshow/from-exploratory-search-to-web-search-and-back-pikm-2010/5611650 pikm2010-slideshare-101029145745-phpapp01
The power of search is with no doubt one of the main aspects for the success of the Web. Currently available search engines on the Web allow to return results with a high precision. Nevertheless, if we limit our attention only to lookup search we are missing another important search task. In exploratory search, the user is willing not only to find documents relevant with respect to her query but she is also interested in learning, discovering and understanding novel knowledge on complex and sometimes unknown topics. In the paper we address this issue presenting LED, a web based system that aims to improve (lookup) Web search by enabling users to properly explore knowledge associated to her query. We rely on DBpedia to explore the semantics of keywords within the query thus suggesting potentially interesting related topics/keywords to the user.]]>
The power of search is with no doubt one of the main aspects for the success of the Web. Currently available search engines on the Web allow to return results with a high precision. Nevertheless, if we limit our attention only to lookup search we are missing another important search task. In exploratory search, the user is willing not only to find documents relevant with respect to her query but she is also interested in learning, discovering and understanding novel knowledge on complex and sometimes unknown topics. In the paper we address this issue presenting LED, a web based system that aims to improve (lookup) Web search by enabling users to properly explore knowledge associated to her query. We rely on DBpedia to explore the semantics of keywords within the query thus suggesting potentially interesting related topics/keywords to the user.]]> Fri, 29 Oct 2010 14:57:39 GMT /slideshow/from-exploratory-search-to-web-search-and-back-pikm-2010/5611650 mirizzi@slideshare.net(mirizzi) From Exploratory Search to Web Search and back - PIKM 2010 mirizzi The power of search is with no doubt one of the main aspects for the success of the Web. Currently available search engines on the Web allow to return results with a high precision. Nevertheless, if we limit our attention only to lookup search we are missing another important search task. In exploratory search, the user is willing not only to find documents relevant with respect to her query but she is also interested in learning, discovering and understanding novel knowledge on complex and sometimes unknown topics. In the paper we address this issue presenting LED, a web based system that aims to improve (lookup) Web search by enabling users to properly explore knowledge associated to her query. We rely on DBpedia to explore the semantics of keywords within the query thus suggesting potentially interesting related topics/keywords to the user. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pikm2010-slideshare-101029145745-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> The power of search is with no doubt one of the main aspects for the success of the Web. Currently available search engines on the Web allow to return results with a high precision. Nevertheless, if we limit our attention only to lookup search we are missing another important search task. In exploratory search, the user is willing not only to find documents relevant with respect to her query but she is also interested in learning, discovering and understanding novel knowledge on complex and sometimes unknown topics. In the paper we address this issue presenting LED, a web based system that aims to improve (lookup) Web search by enabling users to properly explore knowledge associated to her query. We rely on DBpedia to explore the semantics of keywords within the query thus suggesting potentially interesting related topics/keywords to the user.

From Exploratory Search to Web Search and back - PIKM 2010 from Roku

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0 Ranking the Linked Data: the case of DBpedia - ICWE 2010 /slideshow/ranking-the-linked-data-the-case-of-dbpedia-icwe-2010/5611636 icwe2010-slideshare-101029145517-phpapp02
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]]> Fri, 29 Oct 2010 14:55:09 GMT /slideshow/ranking-the-linked-data-the-case-of-dbpedia-icwe-2010/5611636 mirizzi@slideshare.net(mirizzi) Ranking the Linked Data: the case of DBpedia - ICWE 2010 mirizzi <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/icwe2010-slideshare-101029145517-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Ranking the Linked Data: the case of DBpedia - ICWE 2010 from Roku

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0 Semantic Tags Generation and Retrieval for Online Advertising - CIKM 2010 /slideshow/semantic-tags-generation-and-retrieval-for-online-advertising-cikm-2010/5611500 cikm2010-slideshare-101029143021-phpapp01
One of the main problems in online advertising is to display ads which are relevant and appropriate \wrt what the user is looking for. Often search engines fail to reach this goal as they do not consider semantics attached to keywords. In this paper we propose a system that tackles the problem by two different angles: help (i) advertisers to create more efficient ads campaigns and (ii) ads providers to properly match ads content to keywords in search engines. We exploit semantic relations stored in the DBpedia dataset and use an hybrid ranking system to rank keywords and to expand queries formulated by the user. Inputs of our ranking system are (i) the DBpedia dataset; (ii) external information sources such as classical search engine results and social tagging systems. We compare our approach with other RDF similarity measures, proving the validity of our algorithm with an extensive evaluation involving real users.]]>
One of the main problems in online advertising is to display ads which are relevant and appropriate \wrt what the user is looking for. Often search engines fail to reach this goal as they do not consider semantics attached to keywords. In this paper we propose a system that tackles the problem by two different angles: help (i) advertisers to create more efficient ads campaigns and (ii) ads providers to properly match ads content to keywords in search engines. We exploit semantic relations stored in the DBpedia dataset and use an hybrid ranking system to rank keywords and to expand queries formulated by the user. Inputs of our ranking system are (i) the DBpedia dataset; (ii) external information sources such as classical search engine results and social tagging systems. We compare our approach with other RDF similarity measures, proving the validity of our algorithm with an extensive evaluation involving real users.]]> Fri, 29 Oct 2010 14:30:15 GMT /slideshow/semantic-tags-generation-and-retrieval-for-online-advertising-cikm-2010/5611500 mirizzi@slideshare.net(mirizzi) Semantic Tags Generation and Retrieval for Online Advertising - CIKM 2010 mirizzi One of the main problems in online advertising is to display ads which are relevant and appropriate \wrt what the user is looking for. Often search engines fail to reach this goal as they do not consider semantics attached to keywords. In this paper we propose a system that tackles the problem by two different angles: help (i) advertisers to create more efficient ads campaigns and (ii) ads providers to properly match ads content to keywords in search engines. We exploit semantic relations stored in the DBpedia dataset and use an hybrid ranking system to rank keywords and to expand queries formulated by the user. Inputs of our ranking system are (i) the DBpedia dataset; (ii) external information sources such as classical search engine results and social tagging systems. We compare our approach with other RDF similarity measures, proving the validity of our algorithm with an extensive evaluation involving real users. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cikm2010-slideshare-101029143021-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> One of the main problems in online advertising is to display ads which are relevant and appropriate \wrt what the user is looking for. Often search engines fail to reach this goal as they do not consider semantics attached to keywords. In this paper we propose a system that tackles the problem by two different angles: help (i) advertisers to create more efficient ads campaigns and (ii) ads providers to properly match ads content to keywords in search engines. We exploit semantic relations stored in the DBpedia dataset and use an hybrid ranking system to rank keywords and to expand queries formulated by the user. Inputs of our ranking system are (i) the DBpedia dataset; (ii) external information sources such as classical search engine results and social tagging systems. We compare our approach with other RDF similarity measures, proving the validity of our algorithm with an extensive evaluation involving real users.

Semantic Tags Generation and Retrieval for Online Advertising - CIKM 2010 from Roku

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0 A Semantic Web enabled System for Résumé Composition and Publication - SWIM 09 /slideshow/a-semantic-web-enabled-system-for-resume-composition-and-publication-swim-09/2708042 swim-09-091213054155-phpapp01
The process of writing a résumé is a task where the notion of background knowledge plays an important role. In a résumé there is a lot of interrelated and implicit information. The use of domain ontologies and semantic technologies may provide a valuable help to make evident these interrelations and to make explicit the implicit knowledge. We present a system to automatically produce a semantically annotated résumé exploiting domain knowledge modeled with respect to a domain ontology. Here, semantic technologies and domain ontologies have been used both to help the user during the writing process and to explicitly represent domain knowledge in the final CV. The system is available at http://sisinflab.poliba.it/impakt-reloaded/ SWIM’09 - 1st international Workshop on Semantic Web Information Management presented by Roberto Mirizzi (http://sisinflab.poliba.it/mirizzi - roberto.mirizzi -at- gmail.com) Berkeley, CA, USA - September 14, 2009]]>
The process of writing a résumé is a task where the notion of background knowledge plays an important role. In a résumé there is a lot of interrelated and implicit information. The use of domain ontologies and semantic technologies may provide a valuable help to make evident these interrelations and to make explicit the implicit knowledge. We present a system to automatically produce a semantically annotated résumé exploiting domain knowledge modeled with respect to a domain ontology. Here, semantic technologies and domain ontologies have been used both to help the user during the writing process and to explicitly represent domain knowledge in the final CV. The system is available at http://sisinflab.poliba.it/impakt-reloaded/ SWIM’09 - 1st international Workshop on Semantic Web Information Management presented by Roberto Mirizzi (http://sisinflab.poliba.it/mirizzi - roberto.mirizzi -at- gmail.com) Berkeley, CA, USA - September 14, 2009]]> Sun, 13 Dec 2009 05:39:24 GMT /slideshow/a-semantic-web-enabled-system-for-resume-composition-and-publication-swim-09/2708042 mirizzi@slideshare.net(mirizzi) A Semantic Web enabled System for Résumé Composition and Publication - SWIM 09 mirizzi The process of writing a résumé is a task where the notion of background knowledge plays an important role. In a résumé there is a lot of interrelated and implicit information. The use of domain ontologies and semantic technologies may provide a valuable help to make evident these interrelations and to make explicit the implicit knowledge. We present a system to automatically produce a semantically annotated résumé exploiting domain knowledge modeled with respect to a domain ontology. Here, semantic technologies and domain ontologies have been used both to help the user during the writing process and to explicitly represent domain knowledge in the final CV. The system is available at http://sisinflab.poliba.it/impakt-reloaded/ SWIM’09 - 1st international Workshop on Semantic Web Information Management presented by Roberto Mirizzi (http://sisinflab.poliba.it/mirizzi - roberto.mirizzi -at- gmail.com) Berkeley, CA, USA - September 14, 2009 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/swim-09-091213054155-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> The process of writing a résumé is a task where the notion of background knowledge plays an important role. In a résumé there is a lot of interrelated and implicit information. The use of domain ontologies and semantic technologies may provide a valuable help to make evident these interrelations and to make explicit the implicit knowledge. We present a system to automatically produce a semantically annotated résumé exploiting domain knowledge modeled with respect to a domain ontology. Here, semantic technologies and domain ontologies have been used both to help the user during the writing process and to explicitly represent domain knowledge in the final CV. The system is available at http://sisinflab.poliba.it/impakt-reloaded/ SWIM’09 - 1st international Workshop on Semantic Web Information Management presented by Roberto Mirizzi (http://sisinflab.poliba.it/mirizzi - roberto.mirizzi -at- gmail.com) Berkeley, CA, USA - September 14, 2009

A Semantic Web enabled System for R辿sum辿 Composition and Publication - SWIM 09 from Roku

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0 Un sistema web-based per la gestione, la classificazione ed il recupero efficiente della documentazione di scavo /slideshow/un-sistema-webbased-per-la-gestione-la-classificazione-ed-il-recupero-efficiente-della-documentazione-di-scavo/2707916 iremas-091213045358-phpapp01
IV Workshop Italiano "Open Source, Free Software e Open Format nei processi di ricerca archeologica"]]>
IV Workshop Italiano "Open Source, Free Software e Open Format nei processi di ricerca archeologica"]]> Sun, 13 Dec 2009 04:51:14 GMT /slideshow/un-sistema-webbased-per-la-gestione-la-classificazione-ed-il-recupero-efficiente-della-documentazione-di-scavo/2707916 mirizzi@slideshare.net(mirizzi) Un sistema web-based per la gestione, la classificazione ed il recupero efficiente della documentazione di scavo mirizzi IV Workshop Italiano "Open Source, Free Software e Open Format nei processi di ricerca archeologica" <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iremas-091213045358-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> IV Workshop Italiano "Open Source, Free Software e Open Format nei processi di ricerca archeologica"

Un sistema web-based per la gestione, la classificazione ed il recupero efficiente della documentazione di scavo from Roku

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0 https://cdn.slidesharecdn.com/profile-photo-mirizzi-48x48.jpg?cb=1522872646 Expert in Recommender Systems leveraging Semantic technologies and Machine Learning. www.linkedin.com/in/robertomirizzi/ https://cdn.slidesharecdn.com/ss_thumbnails/recommendersystemsinthelinkeddataera-130715131602-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/recommender-systems-in-the-linked-data-era/24260185 Recommender Systems in... https://cdn.slidesharecdn.com/ss_thumbnails/i-semantics2012-130125174955-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/linked-open-data-to-support-contentbased-recommender-systems-isemantic/16183010 Linked Open Data to Su... https://cdn.slidesharecdn.com/ss_thumbnails/iir2012-120306131316-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/movie-recommendation-with-dbpedia-iir-2012/11891071 Movie Recommendation w...