WindOwls-Adaptive System for the Integration of Recommendation Methods in E-Commerce

Kazienko, Przemysław; Kołodziejski, Paweł

doi:10.1007/11495772_34

Cited by 17 publications

(15 citation statements)

References 9 publications

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“…These recommendation systems can either be general (non personalized) or personalized [66]. The former methods do not consider the characteristics and preferences of the customers, whereas the latter tightly depends on the user profile.…”

Section: Recommendation Systems In Online Social Networkmentioning

confidence: 99%

Personalized Recommendation for Online Social Networks Information: Personal Preferences and Location-Based Community Trends

Khater¹,

Gračanin

Elmongui

2017

IEEE Trans. Comput. Soc. Syst.

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Online social networks are experiencing an explosive growth in recent years in both the number of users and the amount of information shared. The users join these social networks to connect with each other, share, find content and disseminate information by sending short text messages in near realtime. As a result of the growth of social networks, the users are often experiencing information overload since they interact with many other users and read ever increasing content volume. Thus, finding the "matching" users and content is one of the key challenges for social networks sites.Recommendation systems have been proposed to help users cope with information overload by predicting the items that a user may be interested in.The users' preferences are shaped by personal interests. At the same time, users are affected by their surroundings, as determined by their geographically located communities. Accordingly, our approach takes into account both personal interests and local communities. We first propose a new dynamic recommendation system model that provides better customized content to the user.That is, the model provides the user with the most important tweets according to his individual interests. We then analyze how changes in the surrounding environment can affect the user's experience. Specifically, we study how changes in the geographical community preferences can affect the individual user's interests. These community preferences are generally reflected in the localized trending topics. Consequently, we present TrendFusion, an innovative model that analyzes the trends propagation, predicts the localized diffusion of trends in social networks and recommends the most interesting trends to the user. Our performance evaluation demonstrate the effectiveness of the proposed recommendation system and shows that it improves the precision and recall of identifying important tweets by up to 36% and 80%, respectively. Results also show that TrendFusion accurately predicts places in which a trend will appear, with 98% recall and 80% precision.To my great parents.To my wonderful husband, Ahmad.To my lovely kids, Ali and Abdullah.iii Acknowledgments First and foremost, I thank Allah for the numerous blessings He has bestowed upon me throughout my dissertation journey.

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Section: Recommendation Systems In Online Social Networkmentioning

confidence: 99%

Personalized Recommendation for Online Social Networks Information: Personal Preferences and Location-Based Community Trends

Khater¹,

Gračanin

Elmongui

2017

IEEE Trans. Comput. Soc. Syst.

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“…For instance, gift buying and buying for self are different. Result from previous studies showed that the personalization strategies that work are those that incorporate multiple methods/channels for different customers and web visiting scenarios [13].…”

Section: Needs Desire or Fantasies By Engaging Multiple Senses Thromentioning

confidence: 99%

Conceptualization of Online Experience

Chea¹,

Luo²

2015

JSS

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“…Each product is assigned to one or more classes with certain weights and the user is attracted to items from the class closest to their profile. This is attribute based recommendation [11]. In this study, demographic recommendation techniques using IMDB data are used.…”

Section: Demographic Recommender Systemsmentioning

confidence: 99%

Utilization of Demographic Analysis with IMDB User Ratings on the Recommendation of Movies

Bae

Lee

Park

2014

The Journal of Society for e-Business Studies

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Nowadays, overflowing data produced every second from the internet make people to be difficult to search for the useful information. That's why people have invented and developed unique tools that they get some relevant information.In this paper, the recommender system, one of the effective tools, is used and it helps us to get the useful information that we want by using demographic information to predict new items of interest. The demographic recommender system in this paper computes users' similarity using demographic information, age and gender. So we performed demographic analysis on movie ratings on Internet Movie Database (IMDB) web site that movies are rated by thousands of people, where users submitted a movie rating after they watched

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WindOwls-Adaptive System for the Integration of Recommendation Methods in E-Commerce

Cited by 17 publications

References 9 publications

Personalized Recommendation for Online Social Networks Information: Personal Preferences and Location-Based Community Trends

Personalized Recommendation for Online Social Networks Information: Personal Preferences and Location-Based Community Trends

Conceptualization of Online Experience

Utilization of Demographic Analysis with IMDB User Ratings on the Recommendation of Movies

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