Encouraging Inactive Users towards Effective Recommendation

Disagreement amongst users in a social network might occur when some of them have different opinion or preferences towards certain items (e.g. topics). Some of the users in the social network might have dynamic preferences due to certain situations. With these differences in opinion amongst the users, some of the users might decide to become either less-active or inactive in providing their opinions on items for recommendation processes to be possible or effective. The current state of the users will lead to a cold-start problem where the recommender system will be unable to find accurate preference information of the users for a recommendation of new items to be provided to them. It will also be difficult to identify these inactive or less-active users within a group for the recommendation of items to be done effectively. Attempts have been made by several researchers to reduce the cold-start problem using singular value decomposition (SVD) algorithm, but the disagreement problem amongst users will still occur due to the dynamic preferences of the users towards items. It was hypothesized in this thesis that an influence based preference modelling could resolve the disagreement problem. It is possible to encourage less-active or inactive users to become active only if they have been identified with a group of their trustworthy neighbours. A suitable clustering technique that does not require pre-specified parameters (e.g. the number of clusters or the number of cluster members) was needed to accurately identify trustworthy users with groups (i.e. clusters) and also identify exemplars (i.e. Cluster representatives) from each group. Several existing clustering techniques such as Highly connected subgraphs (HCS), Markov clustering and Affinity Propagation (AP) clustering were explored in this thesis to check if they have the capabilities to achieve these required outputs. The suitable clustering technique amongst these techniques that is able to identify exemplars in each cluster could be validated using pattern information of past social activities, estimated trust values or familiarity values. The proposed method for estimating these values was based on psychological theories such as the theory of interpersonal behaviour (TIB) and rational choice theory as it was necessary to predict the trustworthiness behaviour of social users. It will also be revealed that users with high trust values (i.e. Trustworthy users) are not necessarily exemplars of various clusters, but they are more likely to encourage less active users in accepting recommended items preferred by the exemplar of their respective cluster

Effect of Influential Users on Recommendation

Relevant information stored in boundless pool of data source are required for the recommendation provided for users in recommender systems. Current recommender systems still suffer from inaccurate or erroneous predictions for users. This may be due to lack of consensus between users who provide different opinions on items after purchase. However, it is possible that this problem might be due to the users having no/few knowledge on the items or they might have had diverse reasons for previous purchase of the items. Therefore, they decide to either provide untruthful opinions on the items or to not even provide their opinions on the items. This demo paper presents a proposed approach to recommendation, where trust information from the social network can be used to motivate or influence users to contribute their opinions for future recommendation. A new trust metric based on trust features such as familiarity and experience value will be used to identify influential users who will control information flow and motivate the members in their community

Social Trust in a Familiar Community

Most computation of social trust have been used for maintaining communities of individuals, based on their past activities. The behaviour of idle individuals or non-contributors to the communities have been totally ignored in the computation as they might affect the representation of the trust computed for the individuals. If the trust for an individual have been misrepresented, other individuals in the community will erroneously disengage or engage with the individual. In this paper, a new trust metric is proposed which is based on user’s pattern of interaction that will be able to assist other users in their decision making on whether to join or leave a community. Different trust features are analysed to evaluate trust values for each user, which is used to determine the trust communities of the users