{"created":"2020-11-20T04:12:03.088825+00:00","id":6326,"links":{},"metadata":{"_buckets":{"deposit":"9a820bf5-fb14-4e75-9eb3-d0554616a642"},"_deposit":{"created_by":45,"id":"6326","owner":"45","owners":[45],"owners_ext":{"displayname":"","email":"dimennyaung@uit.edu.mm","username":""},"pid":{"revision_id":0,"type":"recid","value":"6326"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/6326","sets":["1582963342780:1605779935331"]},"communities":["uit"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Implementation of Recommender System Using Feature-Based Sentiment Analysis","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Collaborative Filtering (CF)-based recommendation technique helps people to make choices based on the opinions of other people who share similar interests. This technique has been suffering from the problems of data sparsity and cold start because of insufficient user ratings or absence of data about users or items. This can affect the accuracy of the recommendation system. User-generated reviews are a plentiful source of user opinions and interests. The proposed personalized recommendation model uses feature base sentiment analysis using ontology that extracts the semantically related features to find the users’ individual preferences rather than rating scores in order to build user profiles that can be understood by user-based collaborative filtering recommendation model. The proposed model intends to alleviate data sparsity problem and to improve accuracy of recommender system by finding user preferences from review text."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value_mlt":[{"interim":"Collaborative Filtering (CF)"},{"interim":"Data sparsity"},{"interim":"Review text"}]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2020-11-20"}],"displaytype":"preview","filename":"Implementation of Recommender System Using Feature-Based Sentiment Analysis.pdf","filesize":[{"value":"1.4 Mb"}],"format":"application/pdf","license_note":"© 2018 ICAIT","licensetype":"license_note","url":{"url":"https://meral.edu.mm/record/6326/files/Implementation of Recommender System Using Feature-Based Sentiment Analysis.pdf"},"version_id":"f535d0d3-bcd6-4ee3-ab6b-c7c3c50e8cb1"}]},"item_1583103147082":{"attribute_name":"Conference papers","attribute_value_mlt":[{"subitem_acronym":"ICAIT-2018","subitem_c_date":"1-2 November, 2018","subitem_conference_title":"2nd International Conference on Advanced Information Technologies","subitem_place":"Yangon, Myanmar","subitem_session":"Natural Language Processing","subitem_website":"https://www.uit.edu.mm/icait-2018/"}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Nyein Ei Ei Kyaw"},{"subitem_authors_fullname":"Thinn Thinn Wai"}]}]},"item_1583108359239":{"attribute_name":"Upload type","attribute_value_mlt":[{"interim":"Publication"}]},"item_1583108428133":{"attribute_name":"Publication type","attribute_value_mlt":[{"interim":"Conference paper"}]},"item_1583159729339":{"attribute_name":"Publication date","attribute_value":"2018-11-02"},"item_title":"Implementation of Recommender System Using Feature-Based Sentiment Analysis","item_type_id":"21","owner":"45","path":["1605779935331"],"publish_date":"2020-11-20","publish_status":"0","recid":"6326","relation_version_is_last":true,"title":["Implementation of Recommender System Using Feature-Based Sentiment Analysis"],"weko_creator_id":"45","weko_shared_id":-1},"updated":"2022-03-24T23:17:08.908826+00:00"}