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Building Recommendation Engines by Suresh Kumar Gorakala

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Context-aware recommender systems

Over the years there has been an evolution in recommender systems from neighborhood approaches to personalized recommender systems that are targeted to individual users. These personalized recommender systems have become a huge success as this is useful at end user level, and for organizations these systems become catalysts to increase their business.

Though the personalized recommender systems were targeted at the individual user level and provided recommendations based on the personal preferences of the users, there was scope to refine the systems. For example, the same person in different places might have different requirements. Likewise, the same person has different requirements at different times:

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