Integrating Data Mining Techniques and Social Networking into Effective Recommendation Framework for Improved Shopping Experience
Date
2014-07-11
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Abstract
The application of data mining in the shopping domain has received a considerable attention for its key role in improving the marketing quality in the last two decades. The main data mining technique that can be used is association rules mining (ARM) though other techniques like clustering and classification are useful but they are beyond the scope of the work described in this thesis. Market basket analysis (MBA) is the most famous example as an application for ARM. MBA’s applications have designed from retail stores’ perspective to gain the benefit. In our thesis, we have designed and implemented a framework that considers the shopping process from consumers’ perspective to turn it into an interactive process, speed it up, save money, and keep the environment clean. Our proposed solution, backed by experimental results, discovers the frequent items that are usually purchased by the consumer; this helps us to introduce them as recommended items. Also, it helps in finding the nearest stores and introduces a navigational map to be used inside the store. Moreover, our proposed solution has been integrated with the social network analysis concept to improve the shopping process quality.
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Computer Science
Citation
Jarada, T. N. (2014). Integrating Data Mining Techniques and Social Networking into Effective Recommendation Framework for Improved Shopping Experience (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/26576