Moein Abdolmohamad Sagha; Morteza Hendijani Fard; Alireza Kooshki Jahromi
Abstract
This paper aims at comparing the banks’ activities in the social networks using the content analysis method. In this study, 54 profiles for 14 external and internal banks in 4 popular social networks are analyzed. The banks are chosen using the purposive sampling method. Results show that most ...
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This paper aims at comparing the banks’ activities in the social networks using the content analysis method. In this study, 54 profiles for 14 external and internal banks in 4 popular social networks are analyzed. The banks are chosen using the purposive sampling method. Results show that most banks use missions/goals on their social page to explain the bank information. They use the augmented product to present the product information. They use the company image to explain corporate identity. They use meetings and conferences to present bank events. They use music style for their videos. They use lifestyle pictures for their photos. They use information support for customer support. They use intimate/ interactive/ harmonious/ poetic style for their slogan and use their campaign albums for their albums. They also use sport and environmental issues to explain their corporate social responsibility. Moreover, they use services as the bank’s marketing messages. They use news announcements to release information. Furthermore, they use social responsibility and public consultation for socialization and finally, they use public partnerships for interactive customer engagement.
Venus Mohammadi; Mohsen Hosseinzadeh; Mehdi Hosseinzadeh Hosseinzadeh
Abstract
v Recommender systems utilization has proven sales enhancement in most e-commerce platforms. This system objected to provide more options, comfort and flexibility to user which could make him interested, as well as providing better chance for companies to increase sells in their products and services. ...
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v Recommender systems utilization has proven sales enhancement in most e-commerce platforms. This system objected to provide more options, comfort and flexibility to user which could make him interested, as well as providing better chance for companies to increase sells in their products and services. Flourishing popularity of web site has originated intrigue for recommendation systems. By penetrating in infinite fields, recommendation systems give deceptive suggestion on services compatible with user precedence. Integrating recommender systems by data management techniques to can targeted such issues and quality of suggestions will be improved considerably. Recent research reveals an idea of utilizing social network data to refine weakness points of traditional recommender system and improve prediction accuracy and efficiency. In this paper we represent views of recommender systems based on Twitter social network data by usage of variety interfaces, content analysis Methods, computational linguistics techniques and MALLET topic modeling algorithm. By deep exploration of objects, methodologies and available data sources, this paper will helps interested people to develop travel recommendation systems and facilitates future research by achieved direction.
Seyyed Jalaladdin Hosseini Dehshiri; Mojtaba Aghaei; Mohammad’Taghi Taghavifard
Abstract
Recommender systems utilization has proven sales enhancement in most e-commerce platforms. This system objected to provide more options, comfort and flexibility to user which could make him interested, as well as providing better chance for companies to increase sells in their products and services. ...
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Recommender systems utilization has proven sales enhancement in most e-commerce platforms. This system objected to provide more options, comfort and flexibility to user which could make him interested, as well as providing better chance for companies to increase sells in their products and services. Flourishing popularity of web site has originated intrigue for recommendation systems. By penetrating in infinite fields, recommendation systems give deceptive suggestion on services compatible with user precedence. Integrating recommender systems by data management techniques to can targeted such issues and quality of suggestions will be improved considerably. Recent research reveals an idea of utilizing social network data to refine weakness points of traditional recommender system and improve prediction accuracy and efficiency. In this paper we represent views of recommender systems based on Twitter social network data by usage of variety interfaces, content analysis Methods, computational linguistics techniques and MALLET topic modeling algorithm. By deep exploration of objects, methodologies and available data sources, this paper will helps interested people to develop travel recommendation systems and facilitates future research by achieved direction.