Circle-based Group Recommendation in Social Networks
- PDF / 1,570,802 Bytes
- 15 Pages / 595.276 x 790.866 pts Page_size
- 31 Downloads / 211 Views
(0123456789().,-volV) (0123456789().,-volV)
FOCUS
Circle-based Group Recommendation in Social Networks Nirmal Choudhary1 • Sonajharia Minz1 • K. K. Bharadwaj1
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract A large amount of data available on Web has proven to be an immense resource for innovative recommender system (RS) techniques and concepts. The traditional recommender system intended to provide recommendations for a single user. However, in certain domains the recommendation is required for a group of users. As to provide better recommendations for a group of users, we leverage the concept of circles in a network. In this work, we use the genetic algorithm (GA) K_Means clustering algorithm to generate social circles in a network. Then, we compute the status of each user in these overlapping circles. Finally, a circle-based group recommendation approach is used to generate the final group recommendation. The results obtained on the Epinions dataset validate the eminence of the proposed model over traditional approaches of group recommendation. Keywords Group recommender systems Genetic algorithm Social factors Status Circles
1 Introduction Recommender systems (RSs) (Adomavicius and Tuzhilin 2005; Resnick and Varian 1997) are the most prominent application of Web personalization to address the issue of information overload. The increasing craze of online services allows RSs to suggest facilitating preferred items or services with the satisfaction of the user or customer with less effort. Most of the traditional recommendation system techniques focused only on a single-user recommendation. But in everyday life, various activities can be done with groups like watching a movie (O’connor et al. 2001), going to a restaurant (McCarthy 2002), listening to music (McCarthy and Anagnost 1998), traveling with friends (Christensen et al. 2016; Ricci et al. 2002), etc. This situation led to the development of group recommender systems (GRSs). Group recommendation is also as important Communicated by Mariagrazia Dotoli. & Nirmal Choudhary [email protected] Sonajharia Minz [email protected] K. K. Bharadwaj [email protected] 1
School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India
as a traditional recommender system. GRSs aim to select a set of items that are likely of interest to a group of users (Dara et al. 2019; Jameson and Smyth 2007). Recently, the group recommendation became an active research area in various domains with the undue growth of social networking. Generally, the user specifies their preferences in the form of ratings, but this information is not always trustworthy in the Web-based environment (Choudhary and Bharadwaj 2018). The increasing popularity of social networks allows us to follow streams of information generated by hundreds of our friends and acquaintances. Therefore, users need to be careful about the reach of their information to avoid facing social problems. Several techniques are employed by users to determine wh
Data Loading...
