| Description |
1 online resource (201 pages) |
| Contents |
Intro; Foreword; Preface; Contents; Acronyms; List of Figures; 1 Introduction; 1.1 Data Explosion and Information Overload; 1.2 Intelligent Information Access; 1.2.1 Information Retrieval and Information Filtering; 1.2.2 Recommender Systems; 1.3 Why Do We Need Content?; 1.3.1 Tackling the Issues of Collaborative Filtering; 1.3.2 Feed and Follow Recent Trends; 1.4 Why Do We Need Semantics?; References; 2 Basics of Content Representation; 2.1 Pipeline for Text Processing; 2.1.1 Lexical Analysis; 2.1.2 Syntactic Analysis; 2.2 Vector Space Model; 2.3 Semantics-Aware Content Representation |
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4.3.2 Use of Linked Open DataReferences; 5 Adaptive and Personalized Systems Based on Semantics; 5.1 Semantics-Aware Recommender Systems; 5.1.1 Approaches Based on Endogenous Semantics; 5.1.2 Approaches Based on Exogenous Semantics; 5.1.3 Semantics-Aware User Profiling Techniques; 5.2 Semantics-Aware Social Media Analysis; 5.2.1 L'Aquila Social Urban Network; 5.2.2 The Italian Hate Map; 5.3 New Trends and Challenges; 5.3.1 Cross-Lingual Recommendations; 5.3.2 Conversational Recommender Systems; 5.3.3 Explaining Recommendations; 5.3.4 Serendipitous Recommendations; References |
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6 Conclusions and Future Challenges6.1 Conclusions; 6.2 Future Challenges; References; Appendix Appendix Available Tools and Resources; A.1 Libraries and APIs; A.1.1 Libraries for Natural Language Processing; A.1.2 Libraries for Encoding Endogenous Semantics; A.1.3 Libraries for Encoding Exogenous Semantics; A.2 Datasets and Resources; A.2.1 Resources to Feed Endogenous Approaches; A.2.2 Resources to feed Exogenous Approaches; References |
| Summary |
This monograph gives a complete overview of the techniques and the methods for semantics-aware content representation and shows how to apply such techniques in various use cases, such as recommender systems, user profiling and social media analysis. Throughout the book, the authors provide an extensive analysis of the techniques currently proposed in the literature and cover all the available tools and libraries to implement and exploit such methodologies in real-world scenarios. The book first introduces the problem of information overload and the reasons why content-based information needs to be taken into account. Next, the basics of Natural Language Processing are provided, by describing operations such as tokenization, stopword removal, lemmatization, stemming, part-of-speech tagging, along with the main problems and issues. Finally, the book describes the different approaches for semantics-aware content representation: such approaches are split into 'exogenous and 'endogenous ones, depending on whether external knowledge sources as DBpedia or geometrical models and distributional semantics are used, respectively. To conclude, several successful use cases and an extensive list of available tools and resources to implement the approaches are shown. Semantics in Adaptive and Personalised Systems definitely fills the gap between the extensive literature on content-based recommender systems, natural language processing, and the different types of semantics-aware representations |
| Bibliography |
References3 Encoding Endogenous Semantics; 3.1 Distributional Semantics Models; 3.2 Word Embedding Techniques; 3.2.1 Latent Semantic Analysis; 3.2.2 Random Indexing; 3.2.3 Word2Vec; 3.3 Explicit Semantic Analysis; References; 4 Encoding Exogenous Semantics; 4.1 Overview of Structured Knowledge Sources; 4.1.1 WordNet; 4.1.2 BabelNet: An Encyclopedic Dictionary; 4.1.3 Linked Open Data and DBpedia; 4.1.4 Wikidata; 4.2 Linking Item Features to Concepts; 4.2.1 Word Sense Disambiguation; 4.2.2 Entity Linking; 4.3 Linking Items to a Knowledge Graph; 4.3.1 Use of Ontologies |
| Notes |
Print version record |
| Subject |
Semantic computing.
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Adaptive computing systems.
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Adaptive computing systems
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Semantic computing
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| Form |
Electronic book
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| Author |
Musto, Cataldo
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Narducci, Fedelucio
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Semeraro, Giovanni.
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| ISBN |
303005618X |
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9783030056186 |
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