I will present in WIMS conference on 25th of June 2018 a tutorial entitled Context-aware Recommender Systems based on Matrix/Tensor Decompositions and Random Walks on Graphs
This turorial offers a rich blend of theory and practice regarding dimensionality reduction methods and graph mining algorithms, to deal with challenging issues such as scalability, data noise, and sparsity in recommender systems. Matrix and tensor decomposition methods have been proven to be the most accurate (i.e., Netflix prize) and efficient for handling big data. For each method (SVD, SVD++, HOSVD, CUR, etc.) we will provide a detailed theoretical mathematical background and a step-by-step analysis, by using an integrated toy example, which runs throughout all parts of the tutorial, helping the audience to understand clearly the differences among factorization methods. Moreover, this tutorial surveys important research in a new family of recommender systems aimed at serving multi-dimensional social networks. We will provide the related work for similarity search on graphs. We will see the random walk-based algorithms (i.e., PageRank, SimRank, Katz, etc.) that can be used to provide contextual recommendations in multi-dimensional graphs, where there are many participating entities (users, locations, products, and the time dimension).