Publication Date

Fall 12-18-2018

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Robert Chun

Second Advisor

Thomas Austin

Third Advisor

Shravanthi Pachalla

Abstract

Recommendation system is a filtering system that predicts ratings or preferences that a user might have. Recommendation system is an evolved form of our trivial information retrieval systems. In this paper, we present a technique to solve new item cold start problem. New item cold start problem occurs when a new item is added to a shopping website like Amazon.com. There is no metadata for this item, no ratings and no reviews because it’s a new item in the system. Absence of data results in no recommendation or bad recommendations. Our approach to solve new item cold start problem requires only an image of a new item. A deep learning architecture is used to extract feature vector from an image. Using a distance metric, the distance between various image feature vectors are calculated. Finally, the model recommends most similar items to the users.

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