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Enhancing Recommendation using Ranking in Multidimensional Space
Search engines play their vital part in building ranking algorithms. Product Recommendation systems is a business activity which involves ranking to fulfill customer needs among the competitors. In our work, similar queries are extracted using Memory based Collaborative Filtering (MCF) and those individual ranked lists are combined to produce single superior ranked lists using Top-k Event Scanning (TES) approach, a rank aggregation algorithm which employs B+ trees for indexing. Experimental results shows that the performance is achieved 90% more than the other existing methods.
Indexing, Queries, Ranking, Recommendation, Similar, Top-K, Users.
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