DeepAI AI Chat
Log In Sign Up

Top-N recommendations in the presence of sparsity: An NCD-based approach

by   Athanasios N. Nikolakopoulos, et al.
University of Patras

Making recommendations in the presence of sparsity is known to present one of the most challenging problems faced by collaborative filtering methods. In this work we tackle this problem by exploiting the innately hierarchical structure of the item space following an approach inspired by the theory of Decomposability. We view the itemspace as a Nearly Decomposable system and we define blocks of closely related elements and corresponding indirect proximity components. We study the theoretical properties of the decomposition and we derive sufficient conditions that guarantee full item space coverage even in cold-start recommendation scenarios. A comprehensive set of experiments on the MovieLens and the Yahoo!R2Music datasets, using several widely applied performance metrics, support our model's theoretically predicted properties and verify that NCDREC outperforms several state-of-the-art algorithms, in terms of recommendation accuracy, diversity and sparseness insensitivity.


LLFR: A Lanczos-Based Latent Factor Recommender for Big Data Scenarios

The purpose if this master's thesis is to study and develop a new algori...

Boosting Item-based Collaborative Filtering via Nearly Uncoupled Random Walks

Item-based models are among the most popular collaborative filtering app...

Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation

The increasing availability of implicit feedback datasets has raised the...

Using Collaborative Filtering to Recommend Champions in League of Legends

League of Legends (LoL), one of the most widely played computer games in...

HybridSVD: When Collaborative Information is Not Enough

We propose a hybrid algorithm for top-n recommendation task that allows ...

Thematic recommendations on knowledge graphs using multilayer networks

We present a framework to generate and evaluate thematic recommendations...

Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework

Design of recommender systems aimed at achieving high prediction accurac...