Applying Personalized Recommendation for Social Network Marketing

Esmaeili, Leila; Nasiri, Ramin; Minaei‐Bidgoli, Behrouz

doi:10.4018/ijom.2012010105

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Among them, F is the number of hidden factors, each row in matrix P represents the preference of each user for different hidden factors, and each column in matrix Q represents the possibility of assigning each element to different hidden factors [19,20]. For each rating item, the corresponding predicted value can be decomposed from the matrix to obtain…”

Section: Recommendation Algorithm Based On Implicit Semanticmentioning

confidence: 99%

[Retracted] Personalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion

Huang¹

2021

Journal of Sensors

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With the rapid development of mobile Internet, short video has become another darling after traditional webcast in recent years. How to make full use of short video for effective marketing has become a hot issue that academia and industry are paying close attention to. This article is mainly aimed at exploring practical new media through in-depth research and exploration of the specific implementation methods and strategies of short video marketing in social media, based on the advantages and characteristic models of short video marketing in social media. The strategy of short video marketing in social media, and the use of highly in-depth neural network analysis technology for the personalized marketing recommendation system of new media short videos, so as to better promote the use of social media short videos by enterprises or individuals. We have to learn from marketing activities. The experimental results of this article show that when the data volume reaches 80%, the performance of the VRBCH algorithm steadily improves, so the performance of the main F of the VRBCH algorithm is still relatively ideal when the data volume changes. Due to the high dilution of the experimental data set, the amount of data in the VRBCH algorithm has increased sharply by 30% to 35%, but the purchase rate of the marketing recommendation system is as high as 98%. Therefore, the system has high feasibility.

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Section: Recommendation Algorithm Based On Implicit Semanticmentioning

confidence: 99%

[Retracted] Personalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion

Huang¹

2021

Journal of Sensors

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“…As well as traditional marketing strategies, it is important to understand one or some target customer segments as part of social marketing. Just on these users major marketing efforts are concentrated through personalized recommendations, viral marketing, etc. The application scenario of the proposed research work is the addressing of some social media marketing questions measuring the potential traction of the tweets posted by the brand by means of a time‐aware recommender system.…”

Section: Related Workmentioning

confidence: 99%

Social media marketing through time‐aware collaborative filtering

Maio

Fenza

Gallo

et al. 2017

Concurrency and Computation

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SummarySocial media is assuming a crucial role in purchasing decisions, and most companies are using social media for marketing. Making sense of the unstructured information content shared by users along the time is an emerging challenge. We advise that the dynamic nature with which trends and user's interests evolve along the timeline requires the revising of well-assessed methods to address, for instance, recommendation provisioning, information retrieval, and so on. ie, Twitter. 6 In particular, we advise that time awareness is crucial to more effectively estimate user's interests, and thus, to better address social media marketing improving "vanity metrics" of likes and shares that in social marketing remain the most commonly used measures for evaluating success on social media attempting to increase traction, for instance, posting the right message at the right time.Problem. Let us consider a timestamped tweet stream = {tw 1 , … , tw n } in the time window starting at time t s and ending at time t e , and let us suppose topics corresponding to the content of each

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Investigating the Behaviors of Mobile Games and Online Streaming Users for Online Marketing Recommendations

Liao

Chiu

2021

International Journal of Online Marketing

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Mobile games have become online leisure and communication activities, so in recent years, different generation players have gradually become more involved in mobile games and the market has grown. Online streaming refers to entertainment and communicate in the form of a real-time broadcast on the internet, such as mobile gaming apps which create a one-stop game audio-visual interactive online marketing and social media/network platforms. Using a market survey on Taiwan players, a total of 1,020 questionnaires data are incorporated into a relational database. Big data analytics, including cluster analysis and association rules, are used to determine game players' profiles and their preference patterns and rules. Using cluster analysis, the authors divide Taiwan mobile game players into three clusters and then find each group's profiles. In addition, this study develops a rule-based recommendation approach, association rules, for investigating mobile games players' online streaming and purchasing behaviors in terms of online marketing recommendations.

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Applying Personalized Recommendation for Social Network Marketing

Cited by 4 publications

References 15 publications

[Retracted] Personalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion

[Retracted] Personalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion

Social media marketing through time‐aware collaborative filtering

Investigating the Behaviors of Mobile Games and Online Streaming Users for Online Marketing Recommendations

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