{"id":"W3193171431","doi":"10.1142/s0219622021500619","title":"Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model","year":2021,"lang":"en","type":"article","venue":"International Journal of Information Technology & Decision Making","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Recommender system; Computer science; Collaborative filtering; Machine learning; Artificial intelligence; Scalability; Context (archaeology); Similarity (geometry); Recall; Mean squared error; Information retrieval; Image (mathematics); Database; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009238349,0.0001857844,0.0003207148,0.002071352,0.0001671681,0.000577996,0.001114094,0.0001627359,0.00001752793],"category_scores_gemma":[0.0008584725,0.000175666,0.0001267519,0.0009974566,0.00001767515,0.002510254,0.0004942505,0.0004215808,0.00001297499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005931536,"about_ca_system_score_gemma":0.0002757579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004567958,"about_ca_topic_score_gemma":0.000004100417,"domain_scores_codex":[0.9975223,0.00009447198,0.001374916,0.0001869448,0.0006225993,0.0001987925],"domain_scores_gemma":[0.9953579,0.0002779864,0.00140192,0.0002568444,0.00263982,0.00006552465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000226024,0.0001096266,0.002589433,0.00006728985,0.0002806266,0.0002047348,0.00164242,0.1423277,0.008134383,0.04159766,0.0009016437,0.8019185],"study_design_scores_gemma":[0.0006816831,0.0001028582,0.00003833129,0.001126271,0.000007825516,0.0009619062,0.000605781,0.9717498,0.01694543,0.001970899,0.005637576,0.0001716147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03042719,0.00005803389,0.9655403,0.002075508,0.001065337,0.0001306259,0.00001123384,0.0002194079,0.0004723782],"genre_scores_gemma":[0.5633449,0.000007886629,0.4362766,0.000314359,0.00003362371,0.000005437073,0.000005601392,0.000006933833,0.000004598548],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8294222,"threshold_uncertainty_score":0.7163453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01715713640805244,"score_gpt":0.307494911679278,"score_spread":0.2903377752712256,"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."}}