Efficient Prediction of Difficult Keyword Queries using Collaborative Filtering Recommendation Framework

Keyword queries on databases provide easy access to data, but often suffer from low ranking quality, i.e., low precision and/or recall, as shown in recent benchmarks. Collaborative filtering (CF) is significant and admired technology for recommender systems. Recommender frameworks have been turned out to be significant means for web online clients to adapt to the information overload and have ended up a standout amongst the most effective and prevalent tools in electronic commerce. Recommending and personalization are critical ways to deal with combating information overload. Machine Learning is an imperative piece of frameworks for these assignments. Collaborative filtering has issues, substance based routines address these issues integrating both is best. Keywords: Collaborative Filtering; Content-based Recommender System; Neighbor Selection