A Probabilistic Recommendation Method Inspired by Latent Dirichlet Allocation Model

<table>The recommendation performances on MovieLens; our method uses experiential constant parameters: <svg height="10.3396pt" id="M119" style="vertical-align:-1.64084pt" version="1.1" viewbox="-0.0498162 -8.69876 94.5896 10.3396" width="94.5896pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path d="M324 430H196L233 583L223 592L145 529L120 430H54L29 396L31 388H111L56 126C33 15 54 -12 77 -12C137 -12 214 57 250 95L233 119C208 92 155 59 138 59C126 59 120 70 131 125L186 390L298 394L324 430Z" id="g113-117"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="341" vert-adv-y="341"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,8.405,0)"><path d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z" id="g117-34"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="587" vert-adv-y="587"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,20.143,0)"><path d="M384 0V27C293 34 287 42 287 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Mathematical Problems in Engineering

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Figure 2

Figure 2: A Probabilistic Recommendation Method Inspired by Latent Dirichlet Allocation Model 