Personalized Recommendation Algorithm Based on Product Reviews

Under the background of leap-forward development for the internet, e-commerce has played an importantroleinpeople’sdailylife,buthugedatasizeshavealsobroughtproblems,suchasinformation overloadwhichcanbesolvedbyusinga recommendationsystemeffectively.However,with the developmentofthee-commerce,theamountoftheproductcatalogsandusersbecomeslarger,which causeslowerperformanceofthetraditionalrecommendationsystem.Thisarticlecomesupwitha personalizedrecommendationalgorithmbasedonthedataminingofproductreviewstooptimizethe performanceofthenewrecommendationsystem.Featuresoftheproductwereextracted,forwhich theusers’sentimentpolaritywasanalyzed.Thisarticledevelopsarecommendationsystembasedon theuser’spreferencemodelandtheproductfeaturestogettherecommendationresult.Experimental resultsshowthatapersonalizedrecommendationhassignificantlyimprovedtheaccuracyandrecall ratewhencomparedwithatraditionalrecommendationalgorithm. KEywORdS Feature Extraction, Polarity Analysis, Product Reviews, Recommendation Algorithm, Text Mining

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