CHAPTER 3 MODELLING VIRTUAL COMMUNITIES
3.2 COMMUNITY MODELLING APPROACHES
Modelling virtual communities has recently become very popular in different research areas. In user modelling, modelling group of members provides the grounds for generating group
recommendations (Masthoff, 2004). In social networks, community modelling aids the discovery of relationships between people and among communities (Lin et al., 2008). Modelling user interests, relationships, or a group/community, in general, can provide useful insights to inform what support can be offered to community members. Interests can be derived based on items users are sharing, tags/keywords associated with a user, a description provided by the user on his interests, or keywords extracted based on discussions. Similarly, relationships between members are extracted based on user activities in the community (or the group), discussions members engage in, sharing habits within the community/group, or the spread of expertise and knowledge. For the purpose of this PhD approaches from both user modelling and social network areas are considered as important and discussed in this section.
A fairly simple and easy to implement community model is presented in (Cheng and Vassileva, 2006). It is based on a list of topics derived based on the resources members of the VC are sharing. In addition, a reward factor is considered to measure the relevance of each contributed resource to the current topic that the VC is working on. The individual user model consists of the reputation measure of a member in the VC and the data describing the user’s current membership level in order to calculate the reward factor (Cheng and Vassileva, 2006). An earlier work in the same group presented a more elaborate relationship model (Bretzke and Vassileva, 2003), which is the closest to ours but there is a crucial difference. Users’ interests are modelled in (Bretzke and Vassileva, 2003) based on how frequently and how recently users have searched for a specific area from the ACM taxonomy, and user relationships are derived based on any successful download or service that took place between two users. In contrast, our approach employs the metadata of the resources shared in the community along with the ontology and derives a semantically relevant list of interests for every user.
A different approach is followed by Tian et.al. (2001) where the community model represents the interaction activities that happen in the VC (Tian et al., 2001). All interactions are associated to a core lexicon which represents the interests of people in the VC. User interests are modelled according to the interactions each user is performing in the VC and associated to the core lexicon of the VC. Shared interests or relationships are also modelled based on the social interaction activities of users and allied with the lexicon developed. The approach presented in this PhD also models user interests based on resources members are uploading or downloading. However, we exploit semantic enrichment of the uploading/downloading activities by using, in addition to the resource key words, concepts extracted from the ontology. Consequently, the data used to extract the interest similarity relation (InterestSim) are semantically enriched. Moreover, the community model is semantically richer, since it contains more than the interactions between community
members having also the personal hierarchies created by each member, relationship model, cognitive centrality and the ontology which represents the community domain.
User interests have been extensively studied. For example, (Davies et al., 2003) present an approach where user interests are extracted as keywords from the user profiles and other web content shared by a user with the community. An ontology is then accessed where associations are derived with ontology concepts and further recommendations are made to users. Interests are also used in finding relationships between users or connections in social graphs. Li et al (2008) is extracting interests based on the tags users are creating for items posted online. Relationships/associations are derived between users based on their tags even if they are not directly connected by a social graph. The latter approach is similar to the one followed in this project - both approaches consider that members can be connected in interest similarity even if they have not read any resources uploaded by each other.
Furthermore, interests of users are usually associated with expertise especially in social network research (Song et al., 2005; Fu et al., 2007; Lin et al., 2007; Zhang et al., 2007). Zhang et al. (2007) extract shared interests on a discussion structured community based on the posting/replying threads. According to the discussion topics a member of the community is contributing to, his interests and expertise are extracted, based on which user relationships are obtained. Fu et al. (2007) are following a similar method but are mining email communication networks. Relationships are inferred according to the expertise/interests of members, which are extracted from communication recorder on their email conversations. Modelling expertise relations plotted as graphs is also explored by Song et al.(2005). A relational network is extracted according to people’s publications. The expertise/interests of a person are obtained by his previous publications and two people are considered related if they have publications in the same research area. This PhD adds to the above approaches. Our approach does not aim at identifying expertise alone, but also derives a person’s influence in the VC based on the relationships he/she has developed with others, which benefits the VC as a whole.
Recent research employed graph theory to model communities and relationships between members (Hubscher and Puntambekar, 2004; Kay et al., 2006) or members’ interactions in general (Falkowski et al., 2007; Falkowski and Spiliopoulou, 2007). In (Hubscher and Puntambekar, 2004) the individual user model is holding the conceptual understanding of a user and a graph based network is constructed. Similarities are then extracted according to a user’s conceptual understanding, and group models are derived based on the distance between members on a graph. Kay et al. (2006) uses the notion of interaction network to represent relationships between users in
a learning community. Two members are related if they have modified the same resource and hence they appear connected in the interaction graph. Falkowski et al. (2007) consider the exchange of messages as interaction between two users, represented in a graph. A relationship exist between two users if they have engaged in message exchange (Falkowski and Spiliopoulou, 2007). Our work also follows a graph-based approach to model a community. The key contribution of the approach presented in this PhD to graph community modelling is the considering of semantic relationships in addition to the interactions between users, i.e. an edge connecting two members represents their semantic similarity to each other, and the relevance of this link to the community’s domain.
Section 3.3 will outline the computational framework for providing support to knowledge sharing VC and will present the proposed community modelling approach.