Graph Based Recommendations: From Data Representation to Feature Extraction and Application

07/05/2017
by   Amit Tiroshi, et al.
0

Modeling users for the purpose of identifying their preferences and then personalizing services on the basis of these models is a complex task, primarily due to the need to take into consideration various explicit and implicit signals, missing or uncertain information, contextual aspects, and more. In this study, a novel generic approach for uncovering latent preference patterns from user data is proposed and evaluated. The approach relies on representing the data using graphs, and then systematically extracting graph-based features and using them to enrich the original user models. The extracted features encapsulate complex relationships between users, items, and metadata. The enhanced user models can then serve as an input to any recommendation algorithm. The proposed approach is domain-independent (demonstrated on data from movies, music, and business recommender systems), and is evaluated using several state-of-the-art machine learning methods, on different recommendation tasks, and using different evaluation metrics. The results show a unanimous improvement in the recommendation accuracy across tasks and domains. In addition, the evaluation provides a deeper analysis regarding the performance of the approach in special scenarios, including high sparsity and variability of ratings.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/31/2020

Embedding Ranking-Oriented Recommender System Graphs

Graph-based recommender systems (GRSs) analyze the structural informatio...
research
11/06/2021

GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation

Research about recommender systems emerges over the last decade and comp...
research
03/27/2019

Link Stream Graph for Temporal Recommendations

Several researches on recommender systems are based on explicit rating d...
research
05/06/2019

A general graph-based framework for top-N recommendation using content, temporal and trust information

Recommending appropriate items to users is crucial in many e-commerce pl...
research
05/03/2020

FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems

Recommender systems are often biased toward popular items. In other word...
research
08/17/2018

Attainment Ratings for Graph-Query Recommendation

The video game industry is larger than both the film and music industrie...

Please sign up or login with your details

Forgot password? Click here to reset