Who and where: context-aware advertisement recommendation on Twitter

Maio, Carmen De; Gallo, Mariacristina; Hao, Fei; Yang, Erhe

doi:10.1007/s00500-020-05147-5

Cited by 12 publications

(5 citation statements)

References 25 publications

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“…An investigation conducted by Lo and Campos [17] examined how companies are integrating IoT solutions into the relationship marketing approaches to determine whether this combination can enhance business performance and what are the challenges with the changes in disruptive technologies. e context-aware advertising recommendation system developed by De Maio et al [18] uses the analysis theory of the triadic formal concept to deduce users' interests and deliver appealing adverts based on their tweets. It was found that a system developed by Deng et al [19] could automatically adapt advertising material to match specific customers' preferences.…”

Section: Literature Surveymentioning

confidence: 99%

“…e performance of IoT-CFIAR was compared to existing methods such as CAAR, IoT-EDF, and SGS-PAC. Contextual user interest elicitation and the categorization and construction of contextual-aware recommendation algorithms are some of the CAAR drawbacks (De Maio et al, 2021). is drawback has been overcome in this paper using efficient prediction of user interest through MAKNN.…”

Section: Click-rough Ratementioning

confidence: 99%

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Advertising Image Design Skills of E-Commerce Products in the Context of the Internet of Things

Wei

2022

Mobile Information Systems

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E-commerce companies often use image advertising as a marketing approach to introduce potential customers to the goods or services that the business offers. People’s tastes are becoming more diverse and diverse in their range of variance. It is difficult for standard e-commerce commercials that aim their message at everyone to get the results they are looking for. The most significant obstacle that must be conquered in e-commerce is figuring out how to properly communicate an image advertisement to the ideal client for e-commerce in the optimal setting. This is a problem that must be resolved. As a result, in this work, we developed a unique commercial fuzzy picture advertising recommendation system for e-commerce items looking at it from the standpoint of the Internet of things (IoT). Customers who shop online may have their location and browsing history collected by Internet of Things devices. A multiadaptive k-nearest neighbour technique is used to predict the customers’ interests. After that, the suggested system is used to provide customized picture adverts to customers based on the customers’ interests and the locations of their devices. The proposed model’s effectiveness was assessed by using variables such as suggestion efficiency, Ad satisfaction rate, execution time, and click-through rate (CTR). According to the findings, the integrated Internet of Things advertising suggestion system that was developed is effective for targeted image advertising and enhancing client happiness.

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Section: Literature Surveymentioning

confidence: 99%

Section: Click-rough Ratementioning

confidence: 99%

Advertising Image Design Skills of E-Commerce Products in the Context of the Internet of Things

Wei

2022

Mobile Information Systems

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“…The authors used Yelp 2017 and Amazon review datasets and evaluated their model using mean square error (MSE). In [92], Maio et al developed a context-aware recommendation system using the users' tweets and interactions along the timeline. They included contexts such as (location, time) to identify targeted and personalized advertisements.…”

Section: B Context-aware Recommendation Systems In E-commercementioning

confidence: 99%

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

Nawara

Kashef

2021

IEEE Access

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An essential goal of recommendation systems is to provide users with accurate and personalized recommendations that meet their preferences. With the rapid growth of IoT-connected sensors, the availability of contextual information has increased, and this has necessitated the fast development of Context-Aware Recommendation Systems (CARS). Context-Aware recommenders are different from traditional recommenders because of their ability to predict the ratings of target users/items by exploiting the knowledge of contextual information. Context-aware recommenders define the context as any information that characterizes the situations of items and users at a particular interaction. They are essential for some contexts where prediction can be more precise in generating specific personalized recommendations. This paper provides a comprehensive review of context-based recommendation systems in IoT environments, namely IoT-CARS, and sheds light on their requirements, characteristics, and applications. We characterize context-aware recommenders in terms of the different IoT contexts and how these contexts are modeled. We also highlight the used metrics to evaluate the performance of various context-based recommenders.

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“…Twitter offers an application programming interface (API) allows us to collect tweets based on a sample stream, keywords or geo-location, subjected to various collection limits [13], [30]. Due to its public availability, Twitter data has been used for many research works in recent years [31]- [33]. In this work, we used the keywords stream to collect tweets containing the names of various social networks, which are LinkedIn, Instagram, Flickr, Reddit, Tumblr and Pinterest.…”

Section: A Social Media Data Collectionmentioning

confidence: 99%

User Identification across Social Networking Sites using User Profiles and Posting Patterns

Solanki¹,

Lim²,

Harwood³

2021

Preprint

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With the prevalence of online social networking sites (OSNs) and mobile devices, people are increasingly reliant on a variety of OSNs for keeping in touch with family and friends, and using it as a source of information. For example, a user might utilise multiple OSNs for different purposes, such as using Flickr to share holiday pictures with family and friends, and Twitter to post short messages about their thoughts. Identifying the same user across multiple OSNs is an important task as this allows us to understand the usage patterns of users among different OSNs, make recommendations when a user registers for a new OSN, and various other useful applications. To address this problem, we proposed an algorithm based on the multilayer perceptron using various types of features, namely: (i) user profile, such as name, location, description; (ii) temporal distribution of user generated content; and (iii) embedding based on user name, real name and description. Using a Twitter and Flickr dataset of users and their posting activities, we perform an empirical study on how these features affect the performance of user identification across the two OSNs and discuss our main findings based on the different features.

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Who and where: context-aware advertisement recommendation on Twitter

Cited by 12 publications

References 25 publications

Advertising Image Design Skills of E-Commerce Products in the Context of the Internet of Things

Advertising Image Design Skills of E-Commerce Products in the Context of the Internet of Things

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

User Identification across Social Networking Sites using User Profiles and Posting Patterns

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