{"id":"W1966933694","doi":"10.1007/s11042-014-2236-3","title":"Towards context-sensitive collaborative media recommender system","year":2014,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Recommender system; Collaborative filtering; Context (archaeology); Social media; Process (computing); Quality (philosophy); World Wide Web; Biosignal; Cold start (automotive); Multimedia; Information retrieval; Filter (signal processing)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003924875,0.0001797876,0.0002820081,0.00007510754,0.0002317738,0.000262218,0.000364738,0.00009919993,0.000003894863],"category_scores_gemma":[0.00004661228,0.0001532579,0.00004426121,0.0003191087,0.00006698677,0.0003271296,0.0001619311,0.0001263622,0.00004611164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005085619,"about_ca_system_score_gemma":0.00004536695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007877076,"about_ca_topic_score_gemma":0.00003550206,"domain_scores_codex":[0.9987237,0.0001320932,0.0003042117,0.0004443743,0.0001678782,0.0002277681],"domain_scores_gemma":[0.9985163,0.0004532503,0.0001434242,0.0005007765,0.000214847,0.0001714374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001578535,0.00003109718,0.00006335296,0.00002095657,0.00001936299,8.170823e-7,0.001172428,4.965564e-7,0.0001564363,0.2939773,0.003315871,0.7012403],"study_design_scores_gemma":[0.001355738,0.0001425558,0.005641281,0.0001414225,0.00003476358,0.00006303762,0.003356595,0.08940203,0.01096021,0.006059208,0.8819374,0.0009057326],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002330423,0.0001593553,0.9786319,0.002205975,0.0001909347,0.0008330256,0.0000706389,0.0004891954,0.01718596],"genre_scores_gemma":[0.9037401,0.00005707251,0.09433912,0.0004826977,0.0002708374,0.001002819,0.0000343146,0.00001475452,0.00005829248],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9035071,"threshold_uncertainty_score":0.6249676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02328783811459668,"score_gpt":0.255424283557986,"score_spread":0.2321364454433893,"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."}}