52 citations · 109 across the 11 of their papers we have counts for
13 papers
NETpred: Network-based modeling and prediction of multiple connected market indices
Alireza Jafari, Saman Haratizadeh
Market prediction plays a major role in supporting financial decisions. An emerging approach in this domain is to use graphical modeling and analysis to for prediction of next mark…
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…
GCNET: graph-based prediction of stock price movement using graph convolutional network
Alireza Jafari, Saman Haratizadeh
The importance of considering related stocks data for the prediction of stock price movement has been shown in many studies, however, advanced graphical techniques for modeling, em…
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…
U-CNNpred: A Universal CNN-based Predictor for Stock Markets
Ehsan Hoseinzade, Saman Haratizadeh, Arash Khoeini
The performance of financial market prediction systems depends heavily on the quality of features it is using. While researchers have used various techniques for enhancing the stoc…
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…
CNNPred: CNN-based stock market prediction using several data sources
Ehsan Hoseinzade, Saman Haratizadeh
Feature extraction from financial data is one of the most important problems in market prediction domain for which many approaches have been suggested. Among other modern tools, co…
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…