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Recommender System for Retail Industry: Ease customers’ purchase by generating personal purchase carts consisting of relevant and original products
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this study we explore the problem of purchase cart recommendationin the field of retail. How can we push the right customize purchase cart that would consider both habits and serendipity constraints? Recommender Systems application is widely restricted to Internet services providers: movie recommendation, e-commerce, search engine. We brought algorithmic and technological breakthroughs to outdated retail systems while keeping in mind its own specificities: purchase cart rather than single products, restricted interactions between customers and products. After collecting ingenious recommendations methods, we defined two major directions - the correctness and the serendipity - that would serve as discriminant aspects to compare multiple solutions we implemented. We expect our solutions to have beneficial impacts on customers, gaining time and open-mindedness, and gradually obliterate the separation between supermarkets and e-commerce platforms as far as customized experience is concerned.

Place, publisher, year, edition, pages
Keyword [en]
Collaborative Filtering ; Recommender System
National Category
Computer Systems
URN: urn:nbn:se:kth:diva-181907OAI: diva2:901568
Available from: 2016-02-09 Created: 2016-02-08 Last updated: 2016-02-09Bibliographically approved

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