Since at least 1979, when the first Usenet news sharing programs were created, online communities have co-evolved with the growth in computer networking. Today, 25 years later, people share news, information, jokes, music, discussion, pictures, and social support in hundreds of thousands of online communities. People benefit from the presence and activity of others in online communities—from the information and other resources they provide and the conversations they participate in.
Despite the vibrancy of online communities, large numbers of them fail. Participation is often sub-optimal, with only a small minority contributing. In many online groups, participation drops to zero. For example, Butler found that 50% of social, hobby, and work mailing lists had no traffic over a 4-month period [1]. Under-contribution is a problem even in communities that do survive. In a majority of active mailing lists, fewer then 50% of subscribers posted even a single message in a 4-month period [1]. Similarly, on the popular peer-to-peer music sharing service, Gnutella, two-thirds of users share no music files and ten percent provide 87% of all the music [2]. In open source development communities, four percent of members account for 50 percent of answers on a user-to-user help site [3], and four percent of developers contribute 88% of new code and 66% of code fixes [4]. Although not everyone needs to contribute for a group to be successful [5], groups with a large proportion of non-contributors have difficulty providing needed services to members. For example, in open source development environments, bugs are not fixed and enhancements are not delivered. In movie rating groups, obscure movies might not be evaluated. In medical support groups, important problems and treatments might not be discussed. We believe it is an important and difficult challenge to design technical features of online communities and seed their social practices in a way that generates ongoing contributions from a larger fraction of the participants. 数据挖掘研究院
In this paper, we attempt to tackle the problem of undercontribution in an online community called MovieLens[6]. MovieLens is a web-based movie recommender community where members can rate movies, write movie reviews, and receive recommendations for movies. More than 20% of the movies listed in the system have so few ratings that the recommender algorithms cannot make accurate predictions about whether subscribers will like them. Here, the contributions we hope to motivate are ratings of movies, especially rarely-rated movies. 数据挖掘研究院
