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Recommender systems: value creation in aiding consumer decisions by Ankit Jain and Sriyansa S. Dash (Student Project)

By: Jain, Ankit.
Contributor(s): Dash, Sriyansa S.
Material type: materialTypeLabelBookPublisher: Ahmedabad Indian Institute of Management 2007Description: 22 p.Subject(s): Recommender systems | Business analysis | Algorithams | Project report (student)DDC classification: SP 2007/1399 Summary: Recommended systems, as an area of study, straddle across disciplines of computer science, statistics, cognitive sciences and management. We aim to give a comprehensive review of existing systems and future applications for the same. A study of the existing systems divides them into two primary categories: Rating and preference based. Rating based systems are further categorized as collaborative, content or Hybrid systems depending on the underlying algorithms used. Preference based systems are a new approaches in this field that attempt to normalize the disparity in the individual user ratings. Current applications of the above systems exist primarily in the online retail of lifestyle goods. We posit future application of recommender systems in the domain of supply chain optimization, real time physical retail systems and as knowledge management tools inside organizations. We future study the applications of some of the current algorithms, such as structured goal seeking, in real-time systems. As the last part of the study we formulate a complete solution for the implementation of a recommended system based service for physical retail stores. We demarcate the viability of such systems for lifestyle and non-lifestyle products; identify a collaborative algorithm to be used on the point-of-sales data for generating recommending the new products and describe a preliminary delivery mechanism. We also identify clear metrics to measure the efficacy of any such system.
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Item type Current location Collection Call number Status Date due Barcode
Student Project Vikram Sarabhai Library
Students Project
Reference SP 2007/1399 (Browse shelf) Not for loan SP001399

Submitted to Prof. Rajnish Dass

Recommended systems, as an area of study, straddle across disciplines of computer science, statistics, cognitive sciences and management. We aim to give a comprehensive review of existing systems and future applications for the same. A study of the existing systems divides them into two primary categories: Rating and preference based. Rating based systems are further categorized as collaborative, content or Hybrid systems depending on the underlying algorithms used. Preference based systems are a new approaches in this field that attempt to normalize the disparity in the individual user ratings. Current applications of the above systems exist primarily in the online retail of lifestyle goods. We posit future application of recommender systems in the domain of supply chain optimization, real time physical retail systems and as knowledge management tools inside organizations. We future study the applications of some of the current algorithms, such as structured goal seeking, in real-time systems. As the last part of the study we formulate a complete solution for the implementation of a recommended system based service for physical retail stores. We demarcate the viability of such systems for lifestyle and non-lifestyle products; identify a collaborative algorithm to be used on the point-of-sales data for generating recommending the new products and describe a preliminary delivery mechanism. We also identify clear metrics to measure the efficacy of any such system.

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