52 citations · 73 across the 4 of their papers we have counts for
5 papers
GEMRank: Global Entity Embedding For Collaborative Filtering
Arash Khoeini, Bita Shams, Saman Haratizadeh
Recently, word embedding algorithms have been applied to map the entities of recommender systems, such as users and items, to new feature spaces using textual element-context relat…
IteRank: An iterative network-oriented approach to neighbor-based collaborative ranking
Bita Shams, Saman Haratizadeh
Neighbor-based collaborative ranking (NCR) techniques follow three consecutive steps to recommend items to each target user: first they calculate the similarities among users, then…
Reliable graph-based collaborative ranking
Bita Shams, Saman Haratizadeh
GRank is a recent graph-based recommendation approach the uses a novel heterogeneous information network to model users' priorities and analyze it to directly infer a recommendatio…
Graph-based Collaborative Ranking
Bita Shams, Saman Haratizadeh
Data sparsity, that is a common problem in neighbor-based collaborative filtering domain, usually complicates the process of item recommendation. This problem is more serious in co…
SibRank: Signed Bipartite Network Analysis for Neighbor-based Collaborative Ranking
Bita Shams, Saman Haratizadeh
Collaborative ranking is an emerging field of recommender systems that utilizes users' preference data rather than rating values. Unfortunately, neighbor-based collaborative rankin…