Publication Date

Winter 2018

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science


The usage of Internet applications, such as social networking and e-commerce is increasing exponentially, which leads to an increased offered content. Recommender systems help users filter out relevant content from a large pool of available content. The recommender systems play a vital role in today’s internet applications. Collaborative Filtering (CF) is one of the popular technique used to design recommendation systems. This technique recommends new content to users based on preferences that the user and similar users have. However, there are some shortcomings to current CF techniques, which affects negatively the performance of the recommendation models. In recent years, deep learning has achieved great success in natural language processing, computer vision and speech recognition. However, the use of deep learning in recommendation domain is relatively new. In this work, we tackle the shortcomings of collaborative filtering by using deep neural network techniques. Although some recent work has employed deep learning for recommendation, they only focused on modeling content descriptions, such as content information of items and auricular features of audios. Moreover, these models ignore the important factor of collaborative filtering, that is the user-item interaction function, but some models still employ matrix factorization, by using inner product on the latent features of items and users. In this project, the inner product is replaced by a neural network architecture, which learns an user-item interaction function from data. To handle any nonlinearities in the user-item interaction function, a multi-layer perceptron is used. Extensive experiments on two real-world datasets demonstrate improvements made by our model compared to existing popular collaborative filtering techniques. Empirical evidence shows deep learning based recommendation models have better performance.