@inproceedings{kharrat_recommendation_2016,
	title = {Recommendation system based contextual analysis of {Facebook} comment},
	url = {http://dx.doi.org/10.1109/AICCSA.2016.7945792},
	doi = {10.1109/AICCSA.2016.7945792},
	abstract = {This paper present a new recommendation algorithm based on contextual
analysis and new measurements. Social Network is one of the most popular
Web 2.0 applications and related services, like Facebook, have evolved
into a practical means for sharing opinions. Consequently, Social Network
web sites have since become rich data sources for opinion mining. This
paper proposes to introduce external resource from comments posted by
users to predict recommendation and relieve the cold start problem. The
novelty of the proposed approach is that posts are not simply
characterized by an opinion score, as is the case with machine
learning-based classifiers, but instead receive an opinion grade for each
distinct notion in the post. Our approach has been implemented with Java
and Lenskit framework; the study we have conducted on a movie dataset has
shown competitive results. We compared our algorithm to SVD and Slope One
algorithms. We have obtained an improvement of 8\% in precision and recall
as well an improvement of 16\% in RMSE and nDCG.},
	author = {Kharrat, F Ben and Elkhleifi, A and Faiz, R},
	month = nov,
	year = {2016},
	keywords = {Algorithm design and analysis, Classification algorithms, Collaboration, Collaborative filtering, Facebook, Motion pictures, Recommendation system, Recommender systems, Social network, User cold start, User profile},
	pages = {1--6},
}

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The novelty of the proposed approach is that posts are not simply characterized by an opinion score, as is the case with machine learning-based classifiers, but instead receive an opinion grade for each distinct notion in the post. Our approach has been implemented with Java and Lenskit framework; the study we have conducted on a movie dataset has shown competitive results. We compared our algorithm to SVD and Slope One algorithms. We have obtained an improvement of 8% in precision and recall as well an improvement of 16% in RMSE and nDCG.","author":[{"propositions":[],"lastnames":["Kharrat"],"firstnames":["F","Ben"],"suffixes":[]},{"propositions":[],"lastnames":["Elkhleifi"],"firstnames":["A"],"suffixes":[]},{"propositions":[],"lastnames":["Faiz"],"firstnames":["R"],"suffixes":[]}],"month":"November","year":"2016","keywords":"Algorithm design and analysis, Classification algorithms, Collaboration, Collaborative filtering, Facebook, Motion pictures, Recommendation system, Recommender systems, Social network, User cold start, User profile","pages":"1–6","bibtex":"@inproceedings{kharrat_recommendation_2016,\n\ttitle = {Recommendation system based contextual analysis of {Facebook} comment},\n\turl = {http://dx.doi.org/10.1109/AICCSA.2016.7945792},\n\tdoi = {10.1109/AICCSA.2016.7945792},\n\tabstract = {This paper present a new recommendation algorithm based on contextual\nanalysis and new measurements. Social Network is one of the most popular\nWeb 2.0 applications and related services, like Facebook, have evolved\ninto a practical means for sharing opinions. Consequently, Social Network\nweb sites have since become rich data sources for opinion mining. This\npaper proposes to introduce external resource from comments posted by\nusers to predict recommendation and relieve the cold start problem. The\nnovelty of the proposed approach is that posts are not simply\ncharacterized by an opinion score, as is the case with machine\nlearning-based classifiers, but instead receive an opinion grade for each\ndistinct notion in the post. Our approach has been implemented with Java\nand Lenskit framework; the study we have conducted on a movie dataset has\nshown competitive results. We compared our algorithm to SVD and Slope One\nalgorithms. We have obtained an improvement of 8\\% in precision and recall\nas well an improvement of 16\\% in RMSE and nDCG.},\n\tauthor = {Kharrat, F Ben and Elkhleifi, A and Faiz, R},\n\tmonth = nov,\n\tyear = {2016},\n\tkeywords = {Algorithm design and analysis, Classification algorithms, Collaboration, Collaborative filtering, Facebook, Motion pictures, Recommendation system, Recommender systems, Social network, User cold start, User profile},\n\tpages = {1--6},\n}\n\n","author_short":["Kharrat, F B.","Elkhleifi, A","Faiz, R"],"key":"kharrat_recommendation_2016","id":"kharrat_recommendation_2016","bibbaseid":"kharrat-elkhleifi-faiz-recommendationsystembasedcontextualanalysisoffacebookcomment-2016","role":"author","urls":{"Paper":"http://dx.doi.org/10.1109/AICCSA.2016.7945792"},"keyword":["Algorithm design and analysis","Classification algorithms","Collaboration","Collaborative filtering","Facebook","Motion pictures","Recommendation system","Recommender systems","Social network","User cold start","User profile"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://api.zotero.org/users/6655/collections/TJPPJ92X/items?key=VFvZhZXIoHNBbzoLZ1IM2zgf&format=bibtex&limit=100","creationDate":"2020-03-27T02:34:35.371Z","downloads":0,"keywords":["algorithm design and analysis","classification algorithms","collaboration","collaborative filtering","facebook","motion pictures","recommendation system","recommender systems","social network","user cold start","user profile"],"search_terms":["recommendation","system","based","contextual","analysis","facebook","comment","kharrat","elkhleifi","faiz"],"title":"Recommendation system based contextual analysis of Facebook comment","year":2016,"dataSources":["5Dp4QphkvpvNA33zi","jfoasiDDpStqkkoZB","BiuuFc45aHCgJqDLY"]}