A Hybrid Movie Recommender Using Dynamic Fuzzy Clustering

Gürcan, Fatih; Birtürk, Ayşenur

doi:10.1007/978-3-319-22635-4_14

Cited by 5 publications

(4 citation statements)

References 24 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to customers' preference, some studies focused on economic factors, such as customers' savings and e-tailer's profits (Garfinkel et al, 2006;Weng et al, 2009;Jiang et al, 2015). Although the current research improved the recommendation system's efficiency through various ways (Wu et al, 2016;Gurcan and Birturk, 2016), this is new as traditional approaches only regard consumers as passive information receiver. The proposed method also considers potential customers' satisfaction level with the recommended products and it can maximize the customer's satisfaction during the online while-recommending process.…”

Section: Recommendation Systemsmentioning

confidence: 95%

A new recommendation system on the basis of consumer initiative decision based on an associative classification approach

Yin

Guo

Yang³

et al. 2018

IMDS

View full text Add to dashboard Cite

Purpose The purpose of this paper is to improve the customer satisfaction by offering online personalized recommendation system. Design/methodology/approach By employing an innovative associative classification method, this paper is able to predict a customer’s pleasure during the online while-recommending process. Consumers can make an active decision to recommended products. Based on customer’s characteristics, a product will be recommended to the potential buyer if the model predicts that he/she will click to view the product. That is, he/she is satisfied with the recommended product. Finally, the feasibility of the proposed recommendation system is validated through a Taobao shop. Findings The results of the experimental study clearly show that the online personalized recommendation system maximizes the customer’s satisfaction during the online while-recommending process based on an innovative associative classification method on the basis of consumer initiative decision. Originality/value Conventionally, customers are considered as passive recipients of the recommendation system. However, customers are tired of the recommendation system, and they can do nothing sometimes. This paper designs a new recommendation system on the basis of consumer initiative decision. The proposed recommendation system maximizes the customer’s satisfaction during the online while-recommending process.

show abstract

Section: Recommendation Systemsmentioning

confidence: 95%

A new recommendation system on the basis of consumer initiative decision based on an associative classification approach

Yin

Guo

Yang³

et al. 2018

IMDS

View full text Add to dashboard Cite

show abstract

“…For example, e-commerce websites (e.g., Amazon.com) customize their interfaces for large-scale visual searches of information about products, sharing online shopping experiences, and review ratings of a desired product. These aspects are considered factors for research using recommendation techniques to identify suitable products for users [39]. Social media entertainment platforms (e.g., online games, YouTube) that utilize hybrid filtering yield highly accurate predictions in filtering results in combination with a desired item [40].…”

Section: Related Workmentioning

confidence: 99%

Social Media Recommender Systems (SMRS): A Bibliometric Analysis Study 2000–2021

et al. 2022

View full text Add to dashboard Cite

The increasing popularity of social media resources such as blogs, bookmarks, chatrooms, forums and video portals in recent years has attracted diverse users. Following the rise of the Internet, online content has become overloaded, prompting the introduction of recommender systems on social media. As a result, research on the dynamic growth of recommender systems in social media has gained significant traction since the year 2000. Social media recommender systems (SMRS) utilize multiple recommendation fields such as item, user, location, tag, event, tour and game in searching for preferred recommended information. Thus, young research fellows, academic scholars and practitioners must understand the need for SMRS to be complemented with recommendation fields. This requirement underlines the significance of a bibliometric analysis that focuses on social media based on existing publications. Hence, using the Web of Science (WoS) database, this study aimed to gather statistical information on SMRS to help researchers acquire an extensive understanding of such systems. The analysis was conducted by identifying SMRS-related publications and scientometric indicators to assess the growth rate, including the relative growth rate (RGR), doubling time (DT) and the field-normalized citation score (NCSf)-for citation analysis. Overall, this bibliometric study provides relevant measures for comparing and improving the citation rate of publications for new researchers.

show abstract

“…Since there is no opportunity to access the new item attribute, diversity of the recommendation items cannot be guaranteed; this problem is known as over specialization [ 9 ]. In order to solve this problem, researches proposed an online hybrid recommender strategy with dynamic fuzzy clustering based on content boosted collaborative filtering algorithm [ 10 ], and proposed a novel probabilistic method for recommending items in the neighborhood-based collaborative filtering framework [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

A personalized channel recommendation and scheduling system considering both section video clips and full video clips

Lee

2018

PLoS ONE

View full text Add to dashboard Cite

With the convergence of various broadcasting systems, the amount of content available in mobile terminals including IPTV has significantly increased. In this paper, we propose a system that enables users to schedule programs considering both section video clips and full video clips based on the user detection method with similar preference. And, since the system constituting the contents can be classified according to the program, the proposed method can store a program desired by the user, and thus create and schedule a kind of individual channel. Experimental results show that the proposed method has a higher prediction accuracy; this is accomplished by comparing existing channel recommendation methods with the program recommendation methods proposed in this paper.

show abstract

A Hybrid Movie Recommender Using Dynamic Fuzzy Clustering

Cited by 5 publications

References 24 publications

A new recommendation system on the basis of consumer initiative decision based on an associative classification approach

A new recommendation system on the basis of consumer initiative decision based on an associative classification approach

Social Media Recommender Systems (SMRS): A Bibliometric Analysis Study 2000–2021

A personalized channel recommendation and scheduling system considering both section video clips and full video clips

Contact Info

Product

Resources

About