Recommender system based on social influence and the virtual house bandwagon effect in virtual worlds

Abstract

Purpose -Due to the rapidly increasing volume of users and products in virtual worlds, recommender systems are an important feature in virtual worlds; they can help solve information overload problems. Virtual world users are able to perform several actions that promote the enjoyment of their virtual life, including interacting with others, visiting virtual houses and shopping for virtual products. This study aims to concentrate on the following two important factors: the social neighbors' influences and the virtual house bandwagon phenomenon, which affects users' preferences during their virtual house visits and purchasing processes.Design/methodology/approach -The authors determine social influence by considering the interactions between the target user and social circle neighbors. The degree of influence of the virtual house bandwagon effect is derived by analyzing the preferences of the virtual house hosts who have been visited by target users during their successive visits. A novel hybrid recommendation method is proposed herein to predict users' preferences by combining the analyses of both factors.Findings -The recommendation performance of the proposed method is evaluated by conducting experiments with a data set collected from a virtual world platform. The experimental results show that the proposed method outperforms the conventional recommendation methods, and they also exhibit the effectiveness of considering both the social influence and the virtual house bandwagon effect for making effective recommendations.Originality/value -Existing studies on recommendation methods did not investigate the virtual house bandwagon effects that are unique to the virtual worlds. The novel idea of the virtual house bandwagon effect is proposed and analyzed for predicting users' preferences. Moreover, a novel hybrid recommendation approach is proposed herein for generating virtual product recommendations. The proposed approach is able to improve the accuracy of preference predictions and enhance the innovative value of recommender systems for virtual worlds.

Exploring Consumers’ Interest in Choosing Sustainable Food

“…This shows that individuals' interests are often reshaped by collective interest. In fact, there are several terms specifically designated for the phenomenon, such as bandwagon effects (Liu et al, 2018), conformity (Smith and Haslam, 2017), and herd behavior (Kameda and Hastie, 2015). Therefore, the study hypothesizes that: H2: Individuals' friends and their support increase their level of interest.…”

Advertisement recommendation based on personal interests and ad push fairness

“…Recommendation systems involve a filtering technology based on the users' preferences or interests which filters off the information the user does not need (Alyari and Jafari Navimipour, 2018). There are several domains which apply recommender systems, such as movies (Resnick et al, 1994), knowledge recommendation for workers (Li et al, 2015), destinations and tour services (Yuan and Yang, 2017), documents and books (Liu et al, 2012;Mooney and Roy, 2000), alliance partners (Yuan et al, 2015), colleague recommendations (Hazratzadeh and Navimipour, 2016) and products (Li et al, 2017;Liu et al, 2018). In general, there are some commonly used recommendation methods, including collaborative filtering (CF) and content-based filtering (CBF).…”

Integrating collaborative topic modeling and diversity for movie recommendations during news browsing

“…Recommender systems adopt filtering techniques to solve the problem of information overload for users by analyzing their historical preferences or interests and exploring items they may like (Resnick and Varian, 1997). Nowadays, recommender systems have widely been applied in several domains, such as products (Huang et al, 2019;Li et al, 2017;Liu et al, 2018), music (Patel and Wadhvani, 2018), advertisement (Liu et al, 2019), tasks or knowledge (Li et al, 2015;Zhang and Su, 2018), and tour services (Yuan and Yang, 2017). Recommender systems can be broadly divided into three categories based on how recommendations are provided (Alyari and Jafari Navimipour, 2018).…”