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Personalized location recommendations aim to recommend places that users want to visit, which can save their decision-making time in daily life. However, the recommending task faces a serious data sparsity problem because users have only visited a small part of total places in a city. This problem directly leads to the difficulty in learning latent representations of users and locations. In order to tackle the data sparsity problem and make better recommendations, users’ app usage records in different locations are introduced to compensated for both users’ interests and locations’ characteristics in this paper. An attributed graph-based representation model is proposed to dig out user–app–location associations with high-order features aggregated. Extensive experiments prove that better representations of users and locations are obtained by our proposed model, thus it greatly improves location recommendation performances compared with the state-of-art methods. For example, our model achieves 13.20%, 10.1%, and 9.44% higher performance than the state-of-art (SOTA) models in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>T</mi><mi>o</mi><mi>p</mi><mn>3</mn><mspace width="3.33333pt"></mspace><mi>H</mi><mi>i</mi><mi>t</mi><mi>r</mi><mi>a</mi><mi>t</mi><mi>e</mi></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>T</mi><mi>o</mi><mi>p</mi><mn>3</mn><mspace width="3.33333pt"></mspace><mi>A</mi><mi>c</mi><mi>c</mi><mi>u</mi><mi>r</mi><mi>a</mi><mi>c</mi><mi>y</mi></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi><mi>D</mi><mi>C</mi><msub><mi>G</mi><mn>3</mn></msub></mrow></semantics></math></inline-formula>, respectively, in the Telecom dataset. In the TalkingData dataset, our model achieves 9.34%, 13.35%, and 8.56% better performance than the SOTA models in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>T</mi><mi>o</mi><mi>p</mi><mn>2</mn><mspace width="3.33333pt"></mspace><mi>H</mi><mi>i</mi><mi>t</mi><mi>r</mi><mi>a</mi><mi>t</mi><mi>e</mi></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>T</mi><mi>o</mi><mi>p</mi><mn>2</mn><mspace width="3.33333pt"></mspace><mi>A</mi><mi>c</mi><mi>c</mi><mi>u</mi><mi>r</mi><mi>a</mi><mi>c</mi><mi>y</mi></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi><mi>D</mi><mi>C</mi><msub><mi>G</mi><mn>2</mn></msub></mrow></semantics></math></inline-formula>, respectively. Furthermore, numerical results demonstrate that our model can effectively alleviate the data sparsity problem in recommendation systems.