{"paper":{"title":"Preference-based Graphic Models for Collaborative Filtering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"ChengXiang Zhai, Luo Si, Rong Jin","submitted_at":"2012-10-19T15:06:09Z","abstract_excerpt":"Collaborative filtering is a very useful general technique for exploiting the     preference patterns of a group of users to predict the utility of items to a     particular user. Previous research has studied several probabilistic graphic     models for collaborative filtering with promising results. However, while these     models have succeeded in capturing the similarity among users and items in one     way or the other, none of them has considered the fact that users with similar     interests in items can have very different rating patterns; some users tend to     assign a higher rating "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.2478","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"}