Information Retrieval and Social Media Mining
This book presents diverse contributions related to some of the latest advances in the field of personalization and recommender systems, as well as social media and sentiment analysis. The work comprises several articles that address different problems in these areas by means of recent techniques su...
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| Sonstige: | |
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| Year of Publication: | 2021 |
| Language: | English |
| Physical Description: | 1 electronic resource (144 p.) |
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| 035 | |a (oapen)https://directory.doabooks.org/handle/20.500.12854/68456 | ||
| 035 | |a (EXLCZ)995400000000043401 | ||
| 041 | 0 | |a eng | |
| 100 | 1 | |a Moreno García, María N. |4 edt | |
| 245 | 1 | 0 | |a Information Retrieval and Social Media Mining |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
| 300 | |a 1 electronic resource (144 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 520 | |a This book presents diverse contributions related to some of the latest advances in the field of personalization and recommender systems, as well as social media and sentiment analysis. The work comprises several articles that address different problems in these areas by means of recent techniques such as deep learning, methods to analyze the structure and the dynamics of social networks, and modern language processing approaches for sentiment analysis, among others. The proposals included in the book are representative of some highly topical research directions and cover different application domains where they have been validated. These go from the recommendation of hotels, movies, music, documents, or pharmacy cross-selling to sentiment analysis in the field of telemedicine and opinion mining on news, also including the study of social capital on social media and dynamics aspects of the Twitter social network. | ||
| 546 | |a English | ||
| 650 | 7 | |a History of engineering & technology |2 bicssc | |
| 653 | |a music recommender systems | ||
| 653 | |a social influence | ||
| 653 | |a social trust | ||
| 653 | |a homophily | ||
| 653 | |a collaborative filtering | ||
| 653 | |a streaming services | ||
| 653 | |a ego network | ||
| 653 | |a events | ||
| 653 | |a network dynamics | ||
| 653 | |a Twitter | ||
| 653 | |a hybrid recommender systems | ||
| 653 | |a feedback collection | ||
| 653 | |a digital libraries | ||
| 653 | |a information retrieval | ||
| 653 | |a real-world data | ||
| 653 | |a open-access | ||
| 653 | |a social capital | ||
| 653 | |a social media | ||
| 653 | |a operationalization | ||
| 653 | |a measurement | ||
| 653 | |a scoping review | ||
| 653 | |a graph convolutional neural network | ||
| 653 | |a recommender system | ||
| 653 | |a cross-sales | ||
| 653 | |a pharmacy | ||
| 653 | |a popularity bias | ||
| 653 | |a opinion mining | ||
| 653 | |a opinion summarization | ||
| 653 | |a topic modeling | ||
| 653 | |a semantic similarity measures | ||
| 653 | |a word embeddings | ||
| 653 | |a text mining | ||
| 653 | |a sentiment analysis | ||
| 653 | |a Web-based questionnaire | ||
| 653 | |a telemedicine | ||
| 653 | |a telemonitoring | ||
| 653 | |a telehomecare | ||
| 653 | |a recommender systems | ||
| 653 | |a utility | ||
| 653 | |a multi-criteria | ||
| 653 | |a penalty | ||
| 653 | |a over-expectation | ||
| 653 | |a under-expectation | ||
| 776 | |z 3-0365-0246-7 | ||
| 776 | |z 3-0365-0247-5 | ||
| 700 | 1 | |a Moreno García, María N. |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 05:45:30 Europe/Vienna |f system |c marc21 |a 2022-04-04 09:22:53 Europe/Vienna |g false | ||
| AVE | |i DOAB Directory of Open Access Books |P DOAB Directory of Open Access Books |x https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5338088350004498&Force_direct=true |Z 5338088350004498 |b Available |8 5338088350004498 | ||