Search results for "Social advertising"

showing 3 items of 3 documents

Customer recommendation based on profile matching and customized campaigns in on-line social networks

2019

We propose a general framework for the recommendation of possible customers (users) to advertisers (e.g., brands) based on the comparison between On-Line Social Network profiles. In particular, we associate suitable categories and subcategories to both user and brand profiles in the considered On-line Social Network. When categories involve posts and comments, the comparison is based on word embedding, and this allows to take into account the similarity between the topics of particular interest for a brand and the user preferences. Furthermore, user personal information, such as age, job or genre, are used for targeting specific advertising campaigns. Results on real Facebook dataset show t…

Matching (statistics)Word embeddingInformation retrievalSettore INF/01 - InformaticaSocial networkComputer sciencebusiness.industry02 engineering and technologyRecommender systemProfile matchingSocial advertisingRecommendation systemAdvertising campaignSemantic similaritySemantic similarity020204 information systemsSimilarity (psychology)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingbusinessPersonally identifiable informationProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Identifying the k Best Targets for an Advertisement Campaign via Online Social Networks

2020

We propose a novel approach for the recommendation of possible customers (users) to advertisers (e.g., brands) based on two main aspects: (i) the comparison between On-line Social Network profiles, and (ii) neighborhood analysis on the On-line Social Network. Profile matching between users and brands is considered based on bag-of-words representation of textual contents coming from the social media, and measures such as the Term Frequency-Inverse Document Frequency are used in order to characterize the importance of words in the comparison. The approach has been implemented relying on Big Data Technologies, allowing this way the efficient analysis of very large Online Social Networks. Resul…

Social and Information Networks (cs.SI)FOS: Computer and information sciencesMatching (statistics)Social networkSettore INF/01 - Informaticabusiness.industryComputer scienceBig dataDatabases (cs.DB)AdvertisingComputer Science - Social and Information NetworksOnline Social Networks Social Advertising tf-idf Profile Matching.Term (time)Computer Science - Information RetrievalSet (abstract data type)Computer Science - DatabasesOrder (business)Computer Science - Data Structures and AlgorithmsData Structures and Algorithms (cs.DS)Social mediabusinessRepresentation (mathematics)Information Retrieval (cs.IR)
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Prediction of User-Brand Associations Based on Sentiment Analysis

2023

Finding the right users to be chosen as targets for advertising campaigns is not a trivial task, and it may allow important commercial advantages. A novel approach is presented here for the recommendation of new possible consumers to brands interested in distributing advertising campaigns, ranked according to the “compatibility” between users and brands. A database containing both descriptions associated with different brands, and textual information about users' opinions on different topics, is required in input. Then, sentiment analysis techniques are applied to measure to what extent the users match with the brands, based on the texts associated with their opinions. The approach has been…

social networksSettore INF/01 - Informaticasentiment analysisuser-brand associationssocial advertising
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