{"_buckets": {"deposit": "ba1f0f3f-1a8b-4433-a0a1-0bb2ccb96a7b"}, "_deposit": {"id": "4361", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4361"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4361", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Review Spammer Detection System by Using Rating and Review Content Similarity", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Assessing the trustworthiness of reviews is akey issue for the maintainers of opinion such asAmazon.com. Opinion reviews on products and servicesare used by potential customers before deciding topurchase a product. An important issue that has beenneglected so far is opinion spam or trustworthiness ofonline opinions. To the best of our knowledge, there isstill little published study on this topic, although Webspam and email spam have been investigatedextensively. This paper presents spammer detectiontechniques to calculate spam score of each user basedon their rating and review similarity on the targetproducts. The experiment showed that the presentedtechnique has comparatively spammer detection withless computation than others."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Spam detection"}, {"interim": "rating"}, {"interim": "similarity"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-03"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "9076.pdf", "filesize": [{"value": "63 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 63000.0, "url": {"url": "https://meral.edu.mm/record/4361/files/9076.pdf"}, "version_id": "39e5d4e5-a04f-42e7-b8cb-0e5fc076b6fa"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Ninth International Conference On Computer Applications (ICCA 2011)", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "", "subitem_c_date": "", "subitem_conference_title": "", "subitem_part": "", "subitem_place": "", "subitem_session": "", "subitem_website": ""}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{"subitem_book_title": "", "subitem_isbn": "", "subitem_pages": "", "subitem_place": "", "subitem_publisher": ""}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "", "subitem_supervisor(s)": [{"subitem_supervisor": ""}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Aye, Chan Myae"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2011-05-05"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/230"}, "item_title": "Review Spammer Detection System by Using Rating and Review Content Similarity", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004361", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-03"}, "publish_date": "2019-07-03", "publish_status": "0", "recid": "4361", "relation": {}, "relation_version_is_last": true, "title": ["Review Spammer Detection System by Using Rating and Review Content Similarity"], "weko_shared_id": -1}