A social-semantic recommender system for advertisements |
| |
| Institution: | 1. DIS, Faculty of Computer Science, University of Murcia, 30100 Murcia, Spain;2. Department of Computer Sciences, Østfold University College, Norway;1. Sorbonne Université, CNRS, LIP6, Paris F-75005, France;2. Université Paris-Saclay, CNRS, ISP, ENS Paris-Saclay, Cachan, France;1. RMIT University, Melbourne, Australia;2. Microsoft, Canberra, Australia;3. University of Tsukuba, Tsukuba, Japan |
| |
| Abstract: | 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: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
