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Recommender System Based on 
Modularity 
Maria A. A. Sibaldo1;2, Tiago B. A. de Carvalho1;2, 
Tsang Ing Ren1, George D. C....
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Recommender System Based on Modularity

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Recommender System Based on Modularity poster presented in RecSys Challenge 2014 Workshop, at the RecSys 2014 conference on Oct 10, in Foster City (CA, USA).

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Recommender System Based on Modularity

  1. 1. Recommender System Based on Modularity Maria A. A. Sibaldo1;2, Tiago B. A. de Carvalho1;2, Tsang Ing Ren1, George D. C. Cavalcanti1 1Centro de Informática, UFPE, Recife, Brasil 2Unidade Acadêmica de Garanhuns, UFRPE, Garanhuns, Brasil www.cin.ufpe.br/viisar {maas2, tbac, tir, gdcc}@cin.ufpe.br 1. Introduction This work presents a solution for the RecSys Challenge 2014 by performing a clustering in a bipartite graph whose vertices are of two types: user and item, having the edges as the engagement given to a tweet. The Modularity metric was used to form the groups that contain users and movies. 2. The Proposed Approach 2.1 Bipartite Graph The generated bipartite graph is modeled on an adjacency matrix composed by all nodes of both classes: user and item, this results in a sparse graph shown in Figure 1. 0 0.5 1 1.5 2 2.5 3 3.5 x 104 0 0.5 1 1.5 2 2.5 3 3.5 x 104 non zeros = 15964 Figure 1: Adjacency matrix formed by users (1-22079) and items (22080-35697). Figure 2 shows the power-law degree distribution of the one-mode item graph, then our bipartite graph can be considered a scale-free network (Birmele [2009]). 0 1 2 3 4 5 6 7 8 9 6 4 2 0 One−mode User Graph log(degree) log(frequency) 0 1 2 3 4 5 6 7 8 6 4 2 0 One−mode Item Graph log(degree) log(frequency) Figure 2: Degree distribution of the user and item one-mode graphs. 2.2 Louvain Algorithm and Modularity The Louvain algorithm (Blondel et al. [2008]) is used for clustering the bipartite graph (see Figure 3) based on the Modularity metric (Newman and Girvan [2004]): Q = 1 2m X ij Aij

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