DeepAI AI Chat
Log In Sign Up

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

by   Mirko Polato, et al.

The increasing availability of implicit feedback datasets has raised the interest in developing effective collaborative filtering techniques able to deal asymmetrically with unambiguous positive feedback and ambiguous negative feedback. In this paper, we propose a principled kernel-based collaborative filtering method for top-N item recommendation with implicit feedback. We present an efficient implementation using the linear kernel, and we show how to generalize it to kernels of the dot product family preserving the efficiency. We also investigate on the elements which influence the sparsity of a standard cosine kernel. This analysis shows that the sparsity of the kernel strongly depends on the properties of the dataset, in particular on the long tail distribution. We compare our method with state-of-the-art algorithms achieving good results both in terms of efficiency and effectiveness.


page 1

page 2

page 3

page 4


Boolean kernels for collaborative filtering in top-N item recommendation

In many personalized recommendation problems available data consists onl...

Completing partial recipes using item-based collaborative filtering to recommend ingredients

Increased public interest in healthy lifestyles has motivated the study ...

Learning Item Trees for Probabilistic Modelling of Implicit Feedback

User preferences for items can be inferred from either explicit feedback...

Implicit Feedback Deep Collaborative Filtering Product Recommendation System

In this paper, several Collaborative Filtering (CF) approaches with late...

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

Making recommendations in the presence of sparsity is known to present o...

Using Taste Groups for Collaborative Filtering

Implicit feedback is the simplest form of user feedback that can be used...

Review Regularized Neural Collaborative Filtering

In recent years, text-aware collaborative filtering methods have been pr...