Incorporating Interest Preference and Social Proximity into Collaborative Filtering for Folk Recommendation

In many social communities, it is increasingly popular for people to seek useful information or resources from trusted peers (i.e., folks).In this regard, folk recommendation is no less important than other types of recommendation such as production announcement, movie advertisement, etc.In this paper, user similarity (in terms of interest similarity andsocial proximity) is incorporated with user-based collaborative filtering (CF) for folk recommendation.Specifically, it concerns with recommending folks to a given user in an existing social community network.To this end, a range of similarity-based and CF-based algorithms are evaluated by using two real-world application datasets, demonstrating their potential for effective and efficient folk recommendation.