Publication Date

Fall 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

Keywords

Recommendation system, Cold Start, Deep Learning

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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