{"id":"W2951893969","doi":"10.48550/arxiv.1809.10770","title":"Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Point (geometry); k-nearest neighbors algorithm; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006801484,0.0009294682,0.001108626,0.0005444784,0.0002316751,0.0005931898,0.001364329,0.0008900079,0.0008008796],"category_scores_gemma":[0.002236785,0.0006007651,0.0007999495,0.0006425067,0.0004854361,0.001040844,0.0006802172,0.001448315,0.0006742114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006364678,"about_ca_system_score_gemma":0.0005450652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01503989,"about_ca_topic_score_gemma":0.01853547,"domain_scores_codex":[0.9996356,0.00009391905,0.00001736268,0.000125448,0.00007936234,0.00004833229],"domain_scores_gemma":[0.9990422,0.0005594127,0.00007235025,0.0001047948,0.0001830007,0.0000381665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003957947,0.0003981702,0.006234117,0.0001494501,0.0003654403,0.0002129022,0.0002693354,0.5960616,0.01192069,0.005778946,0.00744719,0.3707663],"study_design_scores_gemma":[0.000004655581,0.00001697001,0.0002641716,0.000004522028,0.00001385472,0.00001553505,0.000003431442,0.9983221,0.0005795371,0.0005521703,0.0002185572,0.000004512206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1133256,0.002692734,0.8762549,0.0007080266,0.0002619946,0.00006123291,0.00034364,0.002155455,0.004196453],"genre_scores_gemma":[0.870764,0.001122511,0.1180931,0.0007094876,0.0002847848,0.00007713577,0.0009640215,0.0001442911,0.007840624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01503989,"threshold_uncertainty_score":0.02990472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1064285033592239,"score_gpt":0.2079724326779185,"score_spread":0.1015439293186946,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}