COMP9417 Machine Learning and Data Mining

Assignment2: Topic 9: Recommender system using collaborative filtering

Introduction

Recommender system is widely used in E-commerce websites that predicts appropriate books, movies, music, website and news for users. Collaborative Filtering (CF) is one of the leading recommending approaches, which analyses existing rating data to compute the similarity between users or items and generates the recommendation list. In our assignment, we focus on user-based collaborative filtering method to predict on users’ movie ratings.

Goals

The goals of this assignment are:
(1) Implement basic user-based CF algorithm to predict users’ ratings on movies.
(2) Improve user-based CF using Top-K users that use the most similar k users to do rating predictions.
(3) Prove that top-K user-based CF performs better than basic CF in terms of accuracy (using mean squared error).

Dataset

In this assignment, we use ‘u.data’ which in the compress folder ‘ml-100k’ downloaded from ‘older datasets: MoiveLens 100K Dataset’. Download link is http://files.grouplens.org/datasets/movielens/ml-100k/u.data

Result

Authors

Jiarui Ding
Quan Yin

2017 S1 COMP9417 DN
Assignment2 25.3/30