{"paper":{"title":"Active Collaborative Filtering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.IR","authors_text":"Benjamin Marlin, Craig Boutilier, Richard S. Zemel","submitted_at":"2012-10-19T15:04:12Z","abstract_excerpt":"Collaborative filtering (CF) allows the preferences of multiple users to be     pooled to make recommendations regarding unseen products. We consider in this     paper the problem of online and interactive CF: given the current ratings     associated with a user, what queries (new ratings) would most improve the     quality of the recommendations made? We cast this terms of expected value of     information (EVOI); but the online computational cost of computing optimal     queries is prohibitive. We show how offline prototyping and computation of     bounds on EVOI can be used to dramatically "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.2442","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}