52 citations · 109 across the 11 of their papers we have counts for
6 papers · 1 filter
STAR: A Session-Based Time-Aware Recommender System
Reza Yeganegi, Saman Haratizadeh
Session-Based Recommenders (SBRs) aim to predict users' next preferences regard to their previous interactions in sessions while there is no historical information about them. Mode…
Attention-Based Recommendation On Graphs
Taher Hekmatfar, Saman Haratizadeh, Parsa Razban +1
Graph Neural Networks (GNN) have shown remarkable performance in different tasks. However, there are a few studies about GNN on recommender systems. GCN as a type of GNNs can extra…
Representation Extraction and Deep Neural Recommendation for Collaborative Filtering
Arash Khoeini, Saman Haratizadeh, Ehsan Hoseinzade
Many Deep Learning approaches solve complicated classification and regression problems by hierarchically constructing complex features from the raw input data. Although a few works…
Embedding Ranking-Oriented Recommender System Graphs
Taher Hekmatfar, Saman Haratizadeh, Sama Goliaei
Graph-based recommender systems (GRSs) analyze the structural information in the graphical representation of data to make better recommendations, especially when the direct user-it…
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…