Exploring Trust-Aware Neighbourhood in Trust-based Recommendation
This addresses the cold-start problem for users in social network-based recommender systems, but it is incremental as it builds on existing trust-based methods.
The paper tackled the problem of cold-start users in recommender systems by incorporating social trust networks, resulting in doubled rating coverage compared to traditional collaborative filtering and near-complete coverage in some datasets.
Traditional Recommender Systems (RS) do not consider any personal user information beyond rating history. Such information, on the other hand, is widely available on social networking sites (Facebook, Twitter). As a result, social networks have recently been used in recommendation systems. In this paper, we propose an efficient method for incorporating social signals into the recommendation process by building a trust network which supplements the users' rating profiles. We first show the effect of different cold-start users types on the Collaborative Filtering (CF) technique in several real-world datasets. Later, we propose a "Trust-Aware Neighbourhood" algorithm which addresses a performance issue of the former by limiting the trusted neighbourhood. We show the doubling of the rating coverage compared to the traditional CF technique, and a significant improvement in the accuracy for some datasets. Focusing specifically on cold-start users, we propose a "Hybrid Trust-Aware Neighbourhood" algorithm which expands the neighbourhood by considering both trust and rating history of the users. We show a near complete coverage with a rich trust network dataset-- Flixster. We conclude by discussing the potential implementation of this algorithm in a budget-constrained cloud environment.