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

Bonomo, Mariella; Ciaccio, Gaspare; Salve, Andrea De; Rombo, Simona E.

doi:10.1145/3341161.3345621

Cited by 6 publications

(7 citation statements)

References 6 publications

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“…In the two analysed contexts, the research behind this PhD project has led to the following main results. The technique focused on Social Networks is applied to brand-affinity matching has been presented in [15]. In particular, the profile-matching technique (presented in Chapter 4) is based on tree-representation of user profiles and applied it on Facebook ego-Networks.…”

Section: Summary Of the Main Resultsmentioning

confidence: 99%

“…The first technique applied to brand-affinity matching that is not an action-based approach has been presented in [15]. In this work [19] the author proposes a general framework for the recommendation of possible customers (users) to advertisers (e.g., brands) based on the comparison between OSN profiles.…”

Section: Semantic Approachesmentioning

confidence: 99%

“…The main goal of the proposed approach [15,19] is to identify the most suitable k possible buyers to whom distributing a given advertisement campaign. To this aim, two important aspects have to be taken into account:…”

Section: A Novel Approach For the Optimization Of An Advertising Camp...mentioning

confidence: 99%

“…In particular, each node in the network points to data associated with a user and retrieved from the considered social media. An important aspect for this research is the textual information available about user general interests and activities, coming for example from private communications, posts, comments, short text messages [15]. Therefore, the user profile of u is represented here by a text T u , characterizing u with references to the considered OSN.…”

Section: A Novel Approach For the Optimization Of An Advertising Camp...mentioning

confidence: 99%

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Knowledge Extraction from Biological and Social Graphs

Bonomo

2022

New Trends in Database and Information Systems

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The research presented in this PhD Thesis has been supported by different research projects. In particular, the PhD scholarship has been funded in part by the PRIN research project "Multicriteria Data Structures and Algorithms: from compressed to learned indexes, and beyond", grant n. 2017WR7SHH (MIUR). Moreover, participation to international conferences for the presentation of some of the obtained results has been possible also thanks to the funds by the GNCS research projects "Algorithms, methods and software tools for knowledge discovery in the context of Precision Medicine" (2020) and "Big knowledge graphs modelling and analysis for problem solving in the web and biological contexts" (2022, CUP E55F22000270001), provided by INDAM GNCS. All mentioned projects have also allowed to join very stimulating research meetings and events which have pushed toward interaction with other researchers working on related topics.

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Section: Summary Of the Main Resultsmentioning

confidence: 99%

Section: Semantic Approachesmentioning

confidence: 99%

Section: A Novel Approach For the Optimization Of An Advertising Camp...mentioning

confidence: 99%

Section: A Novel Approach For the Optimization Of An Advertising Camp...mentioning

confidence: 99%

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Knowledge Extraction from Biological and Social Graphs

Bonomo

2022

New Trends in Database and Information Systems

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“…The first technique applied to brand-affinity matching that is not an action-based approach has been presented in (Bonomo et al, 2019). In particular, the authors present a profile-matching technique based on tree-representation of user profiles and apply it on Facebook ego-networks.…”

Section: Semantic Approachesmentioning

confidence: 99%

Identifying the $k$ Best Targets for an Advertisement Campaign via Online Social Networks

Bonomo¹,

Placa²,

Rombo³

2020

Preprint

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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. Results on real datasets show that the combination of profile matching and neighborhood analysis is successful in identifying the most suitable set of users to be used as target for a given advertisement campaign.

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Smart algorithmic based web crawling and scraping with template autoupdate capabilities

Khan

Tsaramirsis

Ullah³

et al. 2020

Concurrency and Computation

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Summary Web scraping is the process of extracting data from web pages and it is an essential part for the generation of datasets. Currently the field is dominated by capable commercial applications, however, there is always a need for web crawling and web scraping applications for custom projects. Developing fit for purpose tools for retrieving and structuring data from web services, cloud systems, and big data is a challenging task. Based on empirical studies, some of the challenges include structural issues, formatting/ presentation, availability, denial of service, size, and information fetching problems with browsers. Additionally, the data become inaccessible after the structure/template of the website changes for example, after the website update. Thus the dataset cannot be updated in the future without manually modifying the parameters of the Web Scraper. In this paper we propose an algorithm capable of autocorrecting the template (web scraping parameters) used for locating the target data and dealing with some common empirical problems. This is very useful in case there is a need for updating the dataset later, as usually, websites tend to change their pages. Moreover, we introduce an implementation of the algorithm via a tool developed for extracting data from the unity asset store. The tool can capture and store data in XML format. The tool extracted a total of 46 785 (40 611 3D and 6174 2D) items, with 35 successful first retries, 11 second retries and 5 fails.

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

Cited by 6 publications

References 6 publications

Knowledge Extraction from Biological and Social Graphs

Knowledge Extraction from Biological and Social Graphs

Identifying the $k$ Best Targets for an Advertisement Campaign via Online Social Networks

Smart algorithmic based web crawling and scraping with template autoupdate capabilities

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