MS&E 233 Lecture 8: Applications of PageRank to Recommendation Systems

PageRank Revisited: On the Relationship between Node Degrees and Node Significances in Different Applications

Random-walk based techniques, such as PageRank, encode the structure of the graph in the form of a transition matrix of a stochastic process from which significances of the graph nodes can be inferred. Recommendation systems leverage such node significance measures to rank the objects in the database. Context-aware recommendation techniques complement the data graph with additional data that pr...

MS&E 233 Lecture 9 & 10: Network models

PageRank beyond the Web

Google’s PageRank method was developed to evaluate the importance of web-pages via their link structure. The mathematics of PageRank, however, are entirely general and apply to any graph or network in any domain. Thus, PageRank is now regularly used in bibliometrics, social and information network analysis, and for link prediction and recommendation. It’s even used for systems analysis of road ...

A Novel Trust Computation Method Based on User Ratings to Improve the Recommendation

Today, the trust has turned into one of the most beneficial solutions to improve recommender systems, especially in the collaborative filtering method. However, trust statements suffer from a number of shortcomings, including the trust statements sparsity, users' inability to express explicit trust for other users in most of the existing applications, etc. Thus to overcome these problems, this ...

Collaborative Filtering using Euclidean Distance in Recommendation Engine

Objectives: Recommendation engine is a part of information filtering system that tries to predict the ‘preference’ or ‘rating’ of an item in the E-commerce. Recommendation engines have become extremely common in recent days to make an appropriate recommendation rapidly and effectively about any products on which the user is interested. Methods/ Statistical Analysis: One of popular information f...