Adaptive Hybrid Collaborative Filtering Recommendation System (AHCF)

ABSTRACT

Recommendation systems play a vital role in boosting the organization’s profit, especially for ecommerce platforms such as Amazon. These systems focus on targeting specific products to users and predicting user preferences and interests. However, recommendation systems are plagued with many challenges, such as adapting them to changes in user preferences and taste, and the effectiveness of recommendations made also determines the ability to retain and engage new users, as new user conversion to clients. This thesis proposes to use an adaptive hybrid collaborative approach to making recommendations to users. Four algorithms are combined: the Alternate Least Squares (ALS), KMeans clustering, Latent Dirichlet Allocation (LDA) and KMeans streaming. The recommender engine developed is in itself a multi-hybrid system as it not only combines four (4) algorithms but also combines the collaborative technique and content-based techniques of making a recommendation. Thus, the approach adopted can be used on datasets that contain rating information, textual descriptions or both. Three servers are leveraged in the implementation, consisting of the Scala server, PHP and Angular JS server and the MySQL database server for the storage of the results from the recommender engine. Various industry-standard metrics are adopted for the individual algorithms in addition to their computational times. These metrics include Root Mean Square Error(RMSE) for the ALS, Within Cluster Sum of Squares(WCSS) for KMeans, Log Perplexity and Log-Likelihood in the LDA. The memory estimates footprints and computational time on retraining the model are recorded for the KMeans streaming. The recommender engine is tested primarily on the 100K and 1M movieLens datasets and some portions of the 20M dataset are used.