Name
Abstract | ||
|---|---|---|
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 usersu0027 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 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. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Information Retrieval | Recommender system,Data mining,World Wide Web,Collaborative filtering,Social network,Information retrieval,Computer science,User information,Neighbourhood (mathematics),Trust network,Limiting,Cloud computing |
DocType | Volume | Citations |
Journal | abs/1608.05380 | 0 |
PageRank | References | Authors |
0.34 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amira Ghenai | 1 | 10 | 1.51 |
| Moustafa Ghanem | 2 | 538 | 53.05 |