Efficient Hybrid Recommendation Model Based on Content and Collaborative Filtering Approach

Abstract

Recommendation systems are employed in e-commerce and market analysis applications and websites adaptable to customer requirements and interests. These systems analyses trends and people preferences and promote market strategies to enhance businesses. Such recommendation system is built purely with the science of understanding large sets of data generated and collected from the people and can be used to mobilize market trends. In this paper, a novel architectural model for recommendation systems has been proposed. The approach aims at overcoming the limitations of the traditional recommendation Systems. A hybrid of content-based filtering and collaborative based filtering techniques are proposed that spans different item-user parameters for making recommendations. The similarity indices are computed using various mathematical models like cosine similarity, centered cosine similarity etc.

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