A Graph Based Approach for Making Recommendations Based on Multiple Data Sources
Date
2015-05-27
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Abstract
Recommendation system is an information filtering system that predicts customer preferences. Customer preferences are extracted through analyzing the behaviour patterns of customers from multiple data sources. Graph-based models play an important role in recommendation systems to extract the customer preferences from multiple data sources. However, graph-based models have been rarely used in traditional recommendation systems. The main objective of this thesis is to use a graph-based recommender system that uses multiple data sources. A graph-based hybrid recommender model is developed to integrate content-based, collaborative filtering and association rule mining techniques. Moreover, the PageRank algorithm is used to produce a ranked list of recommendation.
Our analysis on a Retail store dataset shows the impact of using multiple data sources on the accuracy of a recommender system while handling the sparsity problem. Usage of demographic information of customers remedies the cold start problem. Grouping the products based on product type produced better results and it also showed the impact of using the different level of product taxonomy. Additionally, assembling content-based, collaborative filtering and association rule mining also showed many improvements in results. Moreover, indirect connections improve the coverage of our recommender system.
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Computer Science
Citation
Dhaliwal, S. (2015). A Graph Based Approach for Making Recommendations Based on Multiple Data Sources (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/24750