A study of friend recommendations for gaming communities. (10th October 2019)
- Record Type:
- Journal Article
- Title:
- A study of friend recommendations for gaming communities. (10th October 2019)
- Main Title:
- A study of friend recommendations for gaming communities
- Authors:
- Watson, Bryan
Watson, Thomas
Zheng, Jun - Abstract:
- Players of online gaming communities such as Steam, Xbox Live, and the PlayStation Network may have trouble finding people to play with as evidenced by the popularity of looking for group (LFG) services. This paper studies friend recommendation systems as a possible solution to alleviate this problem because a high quality friend list can provide a higher chance for the player to find people to play with. An online survey of video game players was conducted to study the need for friend recommendations in gaming communities and how players build their friend lists. The results showed that a sizable portion of players experienced some sort of difficulty finding people to play with and players add friends through a diverse set of possible sources. As the first step to build a friend recommendation system for gaming communities, we tested ten common link prediction similarity indices on a dataset collected from the Xbox Live network containing over 42 million unique users. The results showed that a friend recommendation system-based solely on network topology features did not perform well. Future research should incorporate other information such as player profiles to improve the friend recommendation performance.
- Is Part Of:
- International journal of Web based communities. Volume 15:Number 4(2019)
- Journal:
- International journal of Web based communities
- Issue:
- Volume 15:Number 4(2019)
- Issue Display:
- Volume 15, Issue 4 (2019)
- Year:
- 2019
- Volume:
- 15
- Issue:
- 4
- Issue Sort Value:
- 2019-0015-0004-0000
- Page Start:
- 292
- Page End:
- 314
- Publication Date:
- 2019-10-10
- Subjects:
- friend recommendation -- gaming communities -- looking for group -- LFG -- link prediction -- recommender system
Online social networks -- Periodicals
Social media -- Periodicals
World Wide Web -- Periodicals
302.30285 - Journal URLs:
- http://www.inderscience.com/browse/index.php?journalID=50 ↗
http://www.inderscience.com/ ↗ - Languages:
- English
- ISSNs:
- 1477-8394
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 11873.xml