{"_buckets": {"deposit": "cf4484ee-a773-4210-b226-8e29617780d9"}, "_deposit": {"id": "7886", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "7886"}, "status": "published"}, "_oai": {"id": "oai:kitami-it.repo.nii.ac.jp:00007886", "sets": ["1:86"]}, "author_link": ["40682", "40683", "40684", "40685", "40678", "273", "40680", "40681"], "item_1646810750418": {"attribute_name": "\u51fa\u7248\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_970fb48d4fbd8a85", "subitem_version_type": "VoR"}]}, "item_3_alternative_title_198": {"attribute_name": "\u305d\u306e\u4ed6\u306e\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_alternative_title": "Variable Order Transition Probability Markov Decision Process for the Recommendation System"}]}, "item_3_biblio_info_186": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2013-03", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "1", "bibliographicPageEnd": "30", "bibliographicPageStart": "20", "bibliographicVolumeNumber": "6", "bibliographic_titles": [{"bibliographic_title": "\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u8ad6\u6587\u8a8c\u6570\u7406\u30e2\u30c7\u30eb\u5316\u3068\u5fdc\u7528\uff08TOM\uff09", "bibliographic_titleLang": "ja"}]}]}, "item_3_description_184": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "\u63a8\u85a6\u554f\u984c\u3092\u6271\u3046\u305f\u3081\u306e\u3088\u308a\u4e00\u822c\u5316\u3055\u308c\u305f\u30de\u30eb\u30b3\u30d5\u6c7a\u5b9a\u904e\u7a0b\u30e2\u30c7\u30eb\u306b\u5bfe\u3057\u3066\uff0c\u30d9\u30a4\u30ba\u57fa\u6e96\u306e\u3082\u3068\u3067\u6700\u9069\u306a\u63a8\u85a6\u30eb\u30fc\u30eb\u3092\u5c65\u6b74\u30c7\u30fc\u30bf\u304b\u3089\u6c42\u3081\u308b\u65b9\u6cd5\u3092\u63d0\u6848\u3059\u308b\uff0e\u63a8\u85a6\u554f\u984c\u306b\u95a2\u3059\u308b\u7814\u7a76\u306b\u304a\u3044\u3066\uff0c\u3053\u308c\u307e\u3067\uff0c\u3042\u308b\u5546\u54c1\u3092\u63a8\u85a6\u3057\u305f\u7d50\u679c\u3069\u306e\u5546\u54c1\u304c\u8cb7\u308f\u308c\u305f\u306e\u304b\uff08\u63a8\u85a6\u7d50\u679c\uff09\u3084\uff0c\u3055\u3089\u306b\u306f\uff0c\u4e00\u5b9a\u671f\u9593\u5185\u306b\u884c\u3063\u305f\u8907\u6570\u306e\u63a8\u85a6\u7d50\u679c\u304c\u8003\u616e\u3055\u308c\u308b\u3053\u3068\u306f\u307b\u3068\u3093\u3069\u306a\u304b\u3063\u305f\uff0e\u3053\u308c\u306b\u5bfe\u3057\u3066\uff0c\u30de\u30eb\u30b3\u30d5\u6c7a\u5b9a\u904e\u7a0b\u30e2\u30c7\u30eb\u3092\u7528\u3044\u308b\u3053\u3068\u3067\u4e0a\u8a182\u70b9\u3092\u521d\u3081\u3066\u8003\u616e\u3057\u305f\u624b\u6cd5\u304c\u63d0\u6848\u3055\u308c\u3066\u3044\u308b\uff0e\u63d0\u6848\u6cd5\u306f\uff0c\u305d\u306e\u5f93\u6765\u7814\u7a76\u306e\u30e2\u30c7\u30eb\u3092\u4e00\u822c\u5316\u3057\u305f\u70b9\u306b\u65b0\u898f\u6027\u304c\u3042\u308b\uff0e\u307e\u305f\uff0c\u3082\u30461\u3064\u306e\u65b0\u898f\u6027\u3068\u3057\u3066\uff0c\u63a8\u85a6\u30eb\u30fc\u30eb\u3092\u6c42\u3081\u308b\u305f\u3081\u306e\u30d7\u30ed\u30bb\u30b9\u3092\u7d71\u8a08\u7684\u6c7a\u5b9a\u554f\u984c\u3068\u3057\u3066\u53b3\u5bc6\u306b\u5b9a\u5f0f\u5316\u3057\u305f\u70b9\u304c\u3042\u308b\uff0e\u5f93\u6765\u306e\u30e2\u30c7\u30eb\u3092\u4e00\u822c\u5316\u3059\u308b\u3053\u3068\u3067\uff0c\u30de\u30eb\u30b3\u30d5\u6c7a\u5b9a\u904e\u7a0b\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u63a8\u85a6\u624b\u6cd5\u306e\u9069\u7528\u9818\u57df\u304c\u62e1\u5927\u3055\u308c\uff0c\u304b\u3064\uff0c\u63a8\u85a6\u3059\u308b\u76ee\u7684\u306b\u5bfe\u3057\u3066\u6700\u9069\u306a\u63a8\u85a6\u304c\u884c\u3048\u308b\u3088\u3046\u306b\u306a\u3063\u305f\uff0e\u4eba\u5de5\u30c7\u30fc\u30bf\u3092\u7528\u3044\u305f\u8a55\u4fa1\u5b9f\u9a13\u306b\u3088\u308a\uff0c\u63d0\u6848\u3059\u308b\u63a8\u85a6\u624b\u6cd5\u306e\u6709\u52b9\u6027\u3092\u78ba\u8a8d\u3057\u305f\uff0eIn this paper, we propose a general markov decision process model for the recommendation system. Furthermore, by using historical data, we derive the optimal recommendation lists from the proposed model based on bayesian decision theory. In the recommendation research area, there were little studies that considered both the purchased items and the past recommended items within a given period. In these circumstances, markov decision process based recommend method that can take these two things into account has been proposed. Our method also uses both things as with the previous method. Here, the unique thing about this paper is not only that we generalize the existing model, but also that we formulate the process to get the recommendation lists as the statistical decision problem. As a result, we can obtain the most suitable recommendation lists with respect to the purpose of the recommendation for a wide variety of recommendation scene. By using artificial data, we show the experimental results that our method can obtain more rewards than the conventional method gets.", "subitem_description_type": "Abstract"}]}, "item_3_full_name_183": {"attribute_name": "\u8457\u8005\u5225\u540d", "attribute_value_mlt": [{"nameIdentifiers": [{"nameIdentifier": "40682", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "Kuwata, Shuhei", "nameLang": "en"}]}, {"nameIdentifiers": [{"nameIdentifier": "40683", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "Maeda, Yasunari", "nameLang": "en"}]}, {"nameIdentifiers": [{"nameIdentifier": "40684", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "Matsushima, Toshiyasu", "nameLang": "en"}]}, {"nameIdentifiers": [{"nameIdentifier": "40685", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "Hirasawa, Shigeichi", "nameLang": "en"}]}]}, "item_3_publisher_212": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "\u4e00\u822c\u793e\u56e3\u6cd5\u4eba\u3000\u60c5\u5831\u51e6\u7406\u5b66\u4f1a"}]}, "item_3_select_195": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_3_subject_196": {"attribute_name": "\u65e5\u672c\u5341\u9032\u5206\u985e\u6cd5", "attribute_value_mlt": [{"subitem_subject": "417", "subitem_subject_scheme": "NDC"}]}, "item_access_right": {"attribute_name": "\u30a2\u30af\u30bb\u30b9\u6a29", "attribute_value_mlt": [{"subitem_access_right": "open access", "subitem_access_right_uri": "http://purl.org/coar/access_right/c_abf2"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "\u6851\u7530, \u4fee\u5e73", "creatorNameLang": "ja"}], "nameIdentifiers": [{"nameIdentifier": "40678", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "\u524d\u7530, \u5eb7\u6210", "creatorNameLang": "ja"}], "nameIdentifiers": [{"nameIdentifier": "273", "nameIdentifierScheme": "WEKO"}, {"nameIdentifier": "30422033", "nameIdentifierScheme": "KAKEN - \u7814\u7a76\u8005\u691c\u7d22", "nameIdentifierURI": "https://nrid.nii.ac.jp/ja/nrid/1000030422033/"}]}, {"creatorNames": [{"creatorName": "\u677e\u5d8b, \u654f\u6cf0", "creatorNameLang": "ja"}], "nameIdentifiers": [{"nameIdentifier": "40680", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "\u5e73\u6fa4, \u8302\u4e00", "creatorNameLang": "ja"}], "nameIdentifiers": [{"nameIdentifier": "40681", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2016-11-22"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "No194.pdf", "filesize": [{"value": "512.2 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_note", "mimetype": "application/pdf", "size": 512200.00000000006, "url": {"label": "No194.pdf", "url": "https://kitami-it.repo.nii.ac.jp/record/7886/files/No194.pdf"}, "version_id": "52797f1d-f758-4555-ade3-3cd61423bd29"}]}, "item_keyword": {"attribute_name": "\u30ad\u30fc\u30ef\u30fc\u30c9", "attribute_value_mlt": [{"subitem_subject": "\u63a8\u85a6\u554f\u984c", "subitem_subject_scheme": "Other"}, {"subitem_subject": "\u30de\u30eb\u30b3\u30d5\u6c7a\u5b9a\u904e\u7a0b", "subitem_subject_scheme": "Other"}, {"subitem_subject": "\u30d9\u30a4\u30ba\u6c7a\u5b9a\u7406\u8ad6", "subitem_subject_scheme": "Other"}, {"subitem_subject": "\u5f37\u5316\u5b66\u7fd2", "subitem_subject_scheme": "Other"}, {"subitem_subject": "recommendation", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Markov decision process", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Bayesian decision theory", "subitem_subject_scheme": "Other"}, {"subitem_subject": "reinforcement learning", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "jpn"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "\u63a8\u85a6\u30b7\u30b9\u30c6\u30e0\u306e\u305f\u3081\u306e\u72b6\u614b\u9077\u79fb\u78ba\u7387\u306e\u69cb\u9020\u3092\u672a\u77e5\u3068\u3057\u305f\u30de\u30eb\u30b3\u30d5\u6c7a\u5b9a\u904e\u7a0b", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "\u63a8\u85a6\u30b7\u30b9\u30c6\u30e0\u306e\u305f\u3081\u306e\u72b6\u614b\u9077\u79fb\u78ba\u7387\u306e\u69cb\u9020\u3092\u672a\u77e5\u3068\u3057\u305f\u30de\u30eb\u30b3\u30d5\u6c7a\u5b9a\u904e\u7a0b", "subitem_title_language": "ja"}, {"subitem_title": "Variable Order Transition Probability Markov Decision Process for the Recommendation System", "subitem_title_language": "en"}]}, "item_type_id": "3", "owner": "1", "path": ["1/86"], "permalink_uri": "https://kitami-it.repo.nii.ac.jp/records/7886", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2015-08-17"}, "publish_date": "2015-08-17", "publish_status": "0", "recid": "7886", "relation": {}, "relation_version_is_last": true, "title": ["\u63a8\u85a6\u30b7\u30b9\u30c6\u30e0\u306e\u305f\u3081\u306e\u72b6\u614b\u9077\u79fb\u78ba\u7387\u306e\u69cb\u9020\u3092\u672a\u77e5\u3068\u3057\u305f\u30de\u30eb\u30b3\u30d5\u6c7a\u5b9a\u904e\u7a0b"], "weko_shared_id": -1}