Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation
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.
Code (1)
Tasks
Collaborative FilteringSimilar Papers 제목 키워드 기반
Boolean kernels for collaborative filtering in top-N item recommendation
In many personalized recommendation problems available data consists only of positive interactions (implicit feedback) between users and items. This problem is also known as One-Class Collaborative Filtering (OC-CF). Lin…
Collaborative FilteringQuantitative analysis of Matthew effect and sparsity problem of recommender systems
Recommender systems have received great commercial success. Recommendation has been used widely in areas such as e-commerce, online music FM, online news portal, etc. However, several problems related to input data struc…
Collaborative FilteringRecommendation SystemsExploiting Hierarchy for Ranking-based Recommendation
The purpose of this master's thesis is to study and develop a new algorithmic framework for collaborative filtering (CF) to generate recommendations. The method we propose is based on the exploitation of the hierarchical…
Collaborative FilteringExtending a Tag-based Collaborative Recommender with Co-occurring Information Interests
Collaborative Filtering is largely applied to personalize item recommendation but its performance is affected by the sparsity of rating data. In order to address this issue, recent systems have been developed to improve …
Collaborative FilteringDiversityTAGLeveraging Deep Learning Techniques on Collaborative Filtering Recommender Systems
With the exponentially increasing volume of online data, searching and finding required information have become an extensive and time-consuming task. Recommender Systems as a subclass of information retrieval and decisio…
Collaborative FilteringDeep LearningInformation RetrievalRecommendation Systems+1