Paper Survey And Example Of Collaborative Filtering Implementation In Recommender System

The development of recommender system research has expanded to various applications. Recommender system issues can be analyzed from many perspectives such as user rating strategy, user preferences and text mining. User rating strategy and user preferences are associated with user behavior to find suitable recommended items. Text mining is considered the most related field to database management and web search queries. The relation to the database query, it needs suitable query algorithm web search and user profiling strategy. Our paper survey showed that Latent Semantic Analysis (LSA) method has a better chance to solve recommender system issues especially in web search and user profiling. By comparing with restaurant samples, we describe adequate measures to evaluate the recommender system quality in user profiling. Some algorithm can provide benefits to improve the quality of personalized recommendations that are tailored to user attributes. Further research can provide newer algorithm to handle cold start problem and sparse data from both text mining and mining computation perspectives