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دسته بندی:
سیستم های توصیه گر - recommender systems
سال انتشار:
2019
عنوان انگلیسی مقاله:
A social-semantic recommender system for advertisements
ترجمه فارسی عنوان مقاله:
یک سیستم توصیه گر اجتماعی معنایی برای تبلیغات
منبع:
Sciencedirect - Elsevier - Information Processing and Management, 57 (2019) 102153: doi:10:1016/j:ipm:2019:102153
نویسنده:
Francisco García-Sáncheza,⁎, Ricardo Colomo-Palaciosb, Rafael Valencia-Garcíaa
چکیده انگلیسی:
Social applications foster the involvement of end users in Web content creation, as a result of
which a new source of vast amounts of data about users and their likes and dislikes has become
available. Having access to users’ contributions to social sites and gaining insights into the
consumers’ needs is of the utmost importance for marketing decision making in general, and to
advertisement recommendation in particular. By analyzing this information, advertisement recommendation
systems can attain a better understanding of the users’ interests and preferences,
thus allowing these solutions to provide more precise ad suggestions. However, in addition to the
already complex challenges that hamper the performance of recommender systems (i.e., data
sparsity, cold-start, diversity, accuracy and scalability), new issues that should be considered
have also emerged from the need to deal with heterogeneous data gathered from disparate
sources. The technologies surrounding Linked Data and the Semantic Web have proved effective
for knowledge management and data integration. In this work, an ontology-based advertisement
recommendation system that leverages the data produced by users in social networking sites is
proposed, and this approach is substantiated by a shared ontology model with which to represent
both users’ profiles and the content of advertisements. Both users and advertisement are represented
by means of vectors generated using natural language processing techniques, which
collect ontological entities from textual content. The ad recommender framework has been extensively
validated in a simulated environment, obtaining an aggregated f-measure of 79.2% and
a Mean Average Precision at 3 (MAP@3) of 85.6%.
Keywords: Knowledge-based systems | Recommender systems | Natural language processing | Advertising | Social network services
قیمت: رایگان
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