Neighborhood Aggregation Collaborative Filtering Based on Knowledge Graph

Improved Neighborhood-based Collaborative Filtering

Recommender systems based on collaborative filtering predict user preferences for products or services by learning past user-item relationships. A predominant approach to collaborative filtering is neighborhood based (“k-nearest neighbors"), where a user-item preference rating is interpolated from ratings of similar items and/or users. In this work, we enhance the neighborhood-based approach le...

Collaborative Filtering with Graph-based Implicit Feedback

Introducing consumed items as users’ implicit feedback in matrix factorization (MF) method, SVD++ is one of the most effective collaborative filtering methods for personalized recommender systems. Though powerful, SVD++ has two limitations: (i). only user-side implicit feedback is utilized, whereas item-side implicit feedback, which can also enrich item representations, is not leveraged; (ii). ...

A Collaborative Filtering Tag Recommendation System Based on Graph

With the rapid development of web2.0 technologies, tagging become much more important today to organize information and help users search the information they need with social bookmarking tools. In order to finish the second task of ECML PKDD challenge 2009, we propose a graph-based collaborative filtering tag recommendation system. We also refer to an algorithm called FolkRank, which is an ada...

Neighborhood-based Collaborative Filtering for Therapy Decision Support

Clinical decision support systems (CDSS) providing assistance for diagnosis and treatment decisions are expected to play an increasingly important role in future healthcare. Especially data-driven approaches employing data mining and machine learning techniques to exploit the large volume of daily captured clinical data promise to open up new perspectives. Particularly in e-commerce Recommender...

A Imputed Neighborhood based Collaborative Filtering System for Web Personalization