<?xml version='1.0' encoding='utf-8'?>
<scheme description="In this analysis we apply matrix tri-factorization to the user movie preference data. We will see that movie ratings data can be extremely sparse, as most users rate only a few, if any, movies out of tens of thousands of movies offered by the online media providers, such as Netflix. We then construct the simplest possible relational scheme, i.e. a data fusion graph, of our domain data space that contains a single data matrix. We apply matrix tri-factorization to the ratings data and estimate latent movie ratings data model. The learnt latent model can be mined further, for example, to profile and cluster the movies into genres." title="Matrix Tri-Factorization" version="2.0">
	<nodes>
		<node id="0" name="Fusion Graph" position="(276.0, 451.0)" project_name="Orange3-Data-Fusion" qualified_name="orangecontrib.datafusion.widgets.owfusiongraph.OWFusionGraph" title="Fusion Graph" version="" />
		<node id="1" name="Latent Factors" position="(572.0, 450.0)" project_name="Orange3-Data-Fusion" qualified_name="orangecontrib.datafusion.widgets.owlatentfactors.OWLatentFactors" title="Latent Factors" version="" />
		<node id="2" name="Movie Ratings" position="(89.0, 443.0)" project_name="Orange3-Data-Fusion" qualified_name="orangecontrib.datafusion.widgets.owmovieratings.OWMovieRatings" title="Movie Ratings" version="" />
		<node id="3" name="Distances" position="(765.0, 448.0)" project_name="Orange" qualified_name="Orange.widgets.unsupervised.owdistances.OWDistances" title="Distances" version="" />
		<node id="4" name="Hierarchical Clustering" position="(933.0, 448.0)" project_name="Orange" qualified_name="Orange.widgets.unsupervised.owhierarchicalclustering.OWHierarchicalClustering" title="Hierarchical Clustering" version="" />
		<node id="5" name="Data Table" position="(300, 300)" project_name="Orange" qualified_name="Orange.widgets.data.owtable.OWDataTable" title="Data Table (1)" version="" />
	</nodes>
	<links>
		<link enabled="true" id="0" sink_channel="Fitted fusion graph" sink_node_id="1" source_channel="Fitted Fusion Graph" source_node_id="0" />
		<link enabled="true" id="1" sink_channel="Relation" sink_node_id="0" source_channel="Ratings" source_node_id="2" />
		<link enabled="true" id="2" sink_channel="Data" sink_node_id="3" source_channel="Relation" source_node_id="1" />
		<link enabled="true" id="3" sink_channel="Distances" sink_node_id="4" source_channel="Distances" source_node_id="3" />
		<link enabled="true" id="4" sink_channel="Data" sink_node_id="5" source_channel="Ratings" source_node_id="2" />
	</links>
	<annotations>
		<arrow end="(93.00000000000003, 519.0)" fill="#1F9CDF" id="0" start="(124.00000000000006, 617.0)" />
		<text font-family="Helvetica" font-size="16" id="1" rect="(62.0, 624.0, 169.0, 139.0)">We use user movie preference data in this example.</text>
		<arrow end="(328.0, 270.0)" fill="#1F9CDF" id="2" start="(380.0, 185.0)" />
		<text font-family="Helvetica" font-size="16" id="3" rect="(390.0, 146.0, 227.0, 79.0)">User-by-movie ratings matrix is very sparse. More than 98% of the movie ratings are unknown.</text>
		<arrow end="(283.99999999999994, 518.0)" fill="#1F9CDF" id="4" start="(318.0, 684.0)" />
		<text font-family="Helvetica" font-size="16" id="5" rect="(260.0, 692.0, 205.0, 128.0)">Fusion graph represents the relational structure of our data domain space. Nodes correspond to object types and edges to data matrices.</text>
		<arrow end="(588.1251240679471, 417.46663808150015)" fill="#1F9CDF" id="6" start="(667.1251240679471, 315.46663808150015)" />
		<text font-family="Helvetica" font-size="16" id="7" rect="(632.0, 258.0, 195.0, 76.0)">Open the Latent Factors widget and select the movie recipe matrix.</text>
		<arrow end="(922.0, 528.0)" fill="#1F9CDF" id="8" start="(896.0, 664.0)" />
		<text font-family="Helvetica" font-size="16" id="9" rect="(784.0, 674.0, 199.0, 98.0)">Clustering of the latent movie profiles can reveal groups of related movies, e.g. movies that belong to the same genre.</text>
	</annotations>
	<thumbnail />
	<node_properties>
		<properties format="pickle" node_id="0">gAN9cQAoWBEAAABwcmVmX25faXRlcmF0aW9uc3EBS4BYEwAAAHNhdmVkV2lkZ2V0R2VvbWV0cnlx
AmNzaXAKX3VucGlja2xlX3R5cGUKcQNYDAAAAFB5UXQ0LlF0Q29yZXEEWAoAAABRQnl0ZUFycmF5
cQVDLgHZ0MsAAQAA///35////l7///uWAAABXP//9+f///50///7lgAAAVwAAAABAABxBoVxB4dx
CFJxCVgOAAAAcHJlZl9hbGdvcml0aG1xCksAWAkAAABwcmVmX3JhbmtxC0sFWA4AAABwcmVmX2Fs
Z29fbmFtZXEMWAoAAABPbmUgc291cmNlcQ1YBwAAAGF1dG9ydW5xDolYEwAAAHByZWZfaW5pdGlh
bGl6YXRpb25xD0sCdS4=
</properties>
		<properties format="pickle" node_id="1">gAN9cQAoWBIAAABzZWxlY3RlZF9iYWNrYm9uZXNxAV1xAlgLAAAAY29tcGxldGlvbnNxA11xBFgQ
AAAAc2VsZWN0ZWRfZmFjdG9yc3EFXXEGWBQAAABzZWxlY3RlZF9jb21wbGV0aW9uc3EHXXEIWBMA
AABzYXZlZFdpZGdldEdlb21ldHJ5cQljc2lwCl91bnBpY2tsZV90eXBlCnEKWAwAAABQeVF0NC5R
dENvcmVxC1gKAAAAUUJ5dGVBcnJheXEMQy4B2dDLAAEAAP//+Fj///0////8CgAAAHj///hY///9
Vf///AoAAAB4AAAAAQAAcQ2FcQ6HcQ9ScRBYBwAAAGZhY3RvcnNxEV1xElgJAAAAYmFja2JvbmVz
cRNdcRRYBwAAAGF1dG9ydW5xFYh1Lg==
</properties>
		<properties format="pickle" node_id="2">gAN9cQAoWAcAAABwZXJjZW50cQFLBVgTAAAAc2F2ZWRXaWRnZXRHZW9tZXRyeXECY3NpcApfdW5w
aWNrbGVfdHlwZQpxA1gMAAAAUHlRdDQuUXRDb3JlcQRYCgAAAFFCeXRlQXJyYXlxBUMuAdnQywAB
AAD///sc///+aP///BX///9K///7HP///n7///wV////SgAAAAEAAHEGhXEHh3EIUnEJWAMAAABl
bmRxCk3XB1gGAAAAbWV0aG9kcQtLAFgFAAAAc3RhcnRxDE3VB3Uu
</properties>
		<properties format="pickle" node_id="3">gAN9cQAoWBMAAABzYXZlZFdpZGdldEdlb21ldHJ5cQFjc2lwCl91bnBpY2tsZV90eXBlCnECWAwA
AABQeVF0NC5RdENvcmVxA1gKAAAAUUJ5dGVBcnJheXEEQy4B2dDLAAEAAP///h7///u4///+3///
/Kn///4e///7zv///t////ypAAAAAQAAcQWFcQaHcQdScQhYCgAAAGF1dG9jb21taXRxCYlYBAAA
AGF4aXNxCksAWAoAAABtZXRyaWNfaWR4cQtLBHUu
</properties>
		<properties format="pickle" node_id="4">gAN9cQAoWAcAAABsaW5rYWdlcQFLAVgMAAAAY2x1c3Rlcl9uYW1lcQJYBwAAAENsdXN0ZXJxA1gQ

AGF1dG9jb21taXRxHYhYBwAAAHBydW5pbmdxHksAdS4=
</properties>
		<properties format="pickle" node_id="5">gAN9cQAoWA4AAABjb2xvcl9zZXR0aW5nc3EBTlgSAAAAc2hvd19kaXN0cmlidXRpb25zcQKJWBUA
AABzaG93X2F0dHJpYnV0ZV9sYWJlbHNxA4hYCwAAAGF1dG9fY29tbWl0cQSIWBMAAABzYXZlZFdp
ZGdldEdlb21ldHJ5cQVjc2lwCl91bnBpY2tsZV90eXBlCnEGWAwAAABQeVF0NC5RdENvcmVxB1gK
AAAAUUJ5dGVBcnJheXEIQy4B2dDLAAEAAAAAAAQAAAAXAAAE5gAAAx8AAAAEAAAALQAABOYAAAMf
AAAAAAAAcQmFcQqHcQtScQxYFQAAAHNlbGVjdGVkX3NjaGVtYV9pbmRleHENSwBYCwAAAHNlbGVj
dF9yb3dzcQ6IWA4AAABkaXN0X2NvbG9yX1JHQnEPKEvcS9xL3Ev/dHEQWA4AAABjb2xvcl9ieV9j
bGFzc3ERiHUu
</properties>
	</node_properties>
</scheme>
