Personalization of services can be proclaimed as the fulcrum of today’s industries. Recommender engines are vastly employed to lend personal relevance to the consumers of services. Recommenders are a class of systems built upon the semantics derived off of consumer profiling, quantitative representations of preferences and so on. They draw a lot of their underlying mechanisms from the field of computational methods, that is, the use of mathematical models and methods to effect relevant suggestions. This paper proposes a framework for the creation of a recommender engine that is capable of predicting a rating matrix as well as providing suggestions. The key advantage introduced by the framework is that in addition to collaborative methods it also employs the techniques of topic modelling and fuzzy logic to find latent topics of interest that are not evidently visible as well as latent groupings of particulars modelled. This framework is a three-component engine consisting of an exploratory module augmented with statistical analysis, visualizations and algorithmic analysis, a latent factor analysis module built on the principles of topic modelling, fuzzy c means algorithm and a prediction-suggestion module built on the concept of singular value decomposition. Thus, this engine can be used in emerging problem domains of relevance to today’s society.

Natural Language Processing, Topics Modelling, Recommender Systems, Latent Factor Analysis, Framework

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