Name
Abstract | ||
|---|---|---|
Large scale virtual worlds such as massive multiplayer online games or 3D worlds gained tremendous popularity over the past few years. With the large and ever increasing amount of content available, virtual world users face the information overload problem. To tackle this issue, game-designers usually deploy recommendation services with the aim of making the virtual world a more joyful environment to be connected at. In this context, we present in this paper the results of a project that aims at understanding the mobility patterns of virtual world users in order to derive place recommenders for helping them to explore content more efficiently. Our study focus on the virtual world SecondLife, one of the largest and most prominent in recent years. Since SecondLife is comparable to real-world Location-based Social Networks (LBSNs), i.e., users can both check-in and share visited virtual places, a natural approach is to assume that place recommenders that are known to work well on real-world LBSNs will also work well on SecondLife. We have put this assumption to the test and found out that (i) while collaborative filtering algorithms have compatible performances in both environments, (ii) existing place recommenders based on geographic metadata are not useful in SecondLife. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2792838.2799674 | Conference on Recommender Systems |
Field | DocType | Citations |
Metaverse,Information overload,World Wide Web,Collaborative filtering,Social network,Geospatial metadata,Computer science,Popularity,Algorithm,Natural approach,Multimedia | Conference | 1 |
PageRank | References | Authors |
0.35 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leandro Balby Marinho | 1 | 702 | 35.57 |
| Christoph Trattner | 2 | 466 | 44.79 |
| Denis Parra | 3 | 69 | 12.98 |