@article{8998219,
	title = {Context-{Similarity} {Collaborative} {Filtering} {Recommendation}},
	volume = {8},
	issn = {21693536},
	doi = {10.1109/ACCESS.2020.2973755},
	abstract = {This article proposes a new method to overcome the sparse data problem of the collaborative filtering models (CF models) by considering the homologous relationship between users or items calculated on contextual attributes when we build the CF models. In the traditional CF models, the results are built only based on data from the user's ratings for items. The results of the proposed models are calculated on two factors: (1) the similar factors based on rating values; (2) the similar factors based on contextual attributes. The findings from the experimentation on two datasets DePaulMovie and InCarMusic, show that the proposed models have higher accuracy than the traditional CF models.},
	journal = {IEEE Access},
	author = {Huynh, Hiep Xuan and Phan, Nghia Quoc and Pham, Nghi Mong and Pham, Van Huy and Hoang Son, Le and Abdel-Basset, Mohamed and Ismail, Mahmoud},
	year = {2020},
	keywords = {CIBCF models, CUBCF models, context-similarity matrix, contextual attributes},
	pages = {33342--33351},
}

{"_id":"hHLFwZArNTmfuJyTq","bibbaseid":"huynh-phan-pham-pham-hoangson-abdelbasset-ismail-contextsimilaritycollaborativefilteringrecommendation-2020","author_short":["Huynh, H. X.","Phan, N. Q.","Pham, N. M.","Pham, V. H.","Hoang Son, L.","Abdel-Basset, M.","Ismail, M."],"bibdata":{"bibtype":"article","type":"article","title":"Context-Similarity Collaborative Filtering Recommendation","volume":"8","issn":"21693536","doi":"10.1109/ACCESS.2020.2973755","abstract":"This article proposes a new method to overcome the sparse data problem of the collaborative filtering models (CF models) by considering the homologous relationship between users or items calculated on contextual attributes when we build the CF models. In the traditional CF models, the results are built only based on data from the user's ratings for items. The results of the proposed models are calculated on two factors: (1) the similar factors based on rating values; (2) the similar factors based on contextual attributes. The findings from the experimentation on two datasets DePaulMovie and InCarMusic, show that the proposed models have higher accuracy than the traditional CF models.","journal":"IEEE Access","author":[{"propositions":[],"lastnames":["Huynh"],"firstnames":["Hiep","Xuan"],"suffixes":[]},{"propositions":[],"lastnames":["Phan"],"firstnames":["Nghia","Quoc"],"suffixes":[]},{"propositions":[],"lastnames":["Pham"],"firstnames":["Nghi","Mong"],"suffixes":[]},{"propositions":[],"lastnames":["Pham"],"firstnames":["Van","Huy"],"suffixes":[]},{"propositions":[],"lastnames":["Hoang","Son"],"firstnames":["Le"],"suffixes":[]},{"propositions":[],"lastnames":["Abdel-Basset"],"firstnames":["Mohamed"],"suffixes":[]},{"propositions":[],"lastnames":["Ismail"],"firstnames":["Mahmoud"],"suffixes":[]}],"year":"2020","keywords":"CIBCF models, CUBCF models, context-similarity matrix, contextual attributes","pages":"33342–33351","bibtex":"@article{8998219,\n\ttitle = {Context-{Similarity} {Collaborative} {Filtering} {Recommendation}},\n\tvolume = {8},\n\tissn = {21693536},\n\tdoi = {10.1109/ACCESS.2020.2973755},\n\tabstract = {This article proposes a new method to overcome the sparse data problem of the collaborative filtering models (CF models) by considering the homologous relationship between users or items calculated on contextual attributes when we build the CF models. In the traditional CF models, the results are built only based on data from the user's ratings for items. The results of the proposed models are calculated on two factors: (1) the similar factors based on rating values; (2) the similar factors based on contextual attributes. The findings from the experimentation on two datasets DePaulMovie and InCarMusic, show that the proposed models have higher accuracy than the traditional CF models.},\n\tjournal = {IEEE Access},\n\tauthor = {Huynh, Hiep Xuan and Phan, Nghia Quoc and Pham, Nghi Mong and Pham, Van Huy and Hoang Son, Le and Abdel-Basset, Mohamed and Ismail, Mahmoud},\n\tyear = {2020},\n\tkeywords = {CIBCF models, CUBCF models, context-similarity matrix, contextual attributes},\n\tpages = {33342--33351},\n}\n\n","author_short":["Huynh, H. X.","Phan, N. Q.","Pham, N. M.","Pham, V. H.","Hoang Son, L.","Abdel-Basset, M.","Ismail, M."],"key":"8998219","id":"8998219","bibbaseid":"huynh-phan-pham-pham-hoangson-abdelbasset-ismail-contextsimilaritycollaborativefilteringrecommendation-2020","role":"author","urls":{},"keyword":["CIBCF models","CUBCF models","context-similarity matrix","contextual attributes"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://api.zotero.org/groups/2168152/items?key=VCdsaROd5deDY3prqqG8kI0c&format=bibtex&limit=100","dataSources":["syJjwTDDM32TsM2iF","QwrFbRJvXF69SEShv","HbngRCZLbLed2q9QT","LtEFvT85hYpNg4Esp","iHfnnAr7wKJJxkNMt","PrvBTxn4Zgeep29e5","78Yd9ZHcx783Wkffe","SKRhTA7ok4L4waPkZ","GfrMfnKTkYdcYTRsy","RqqCdXGEyWH4dZ76k","cbiwaQPQJSZeJDDY9","2Jak7xK39ytqcgqQ4","CDfDBPD6CDScj6Ty4","WgiCycoQjRx6KArBy","KBdipwowTNXWiKqYd","yjd6eECyb3TYZpZ3R","D9jmZ7aoHfJnYQ4ES","R8dLFAvyQ2oFRijDJ","dc6SzEK4S9LfC3XpA","kGWABmrDfhF29uibh","YE9GesxGLCsBc3vvC","v3qfuosZ66nvD85FK","BSxBG5ms26R2teZn9"],"keywords":["cibcf models","cubcf models","context-similarity matrix","contextual attributes"],"search_terms":["context","similarity","collaborative","filtering","recommendation","huynh","phan","pham","pham","hoang son","abdel-basset","ismail"],"title":"Context-Similarity Collaborative Filtering Recommendation","year":2020}