Clustering Algorithms in Hybrid Recommender System on MovieLens Data

Abstract

Decisions are taken by humans very often during professional as well as leisure activities. It is particularly evident during surfing the Internet: selecting web sites to explore, choosing needed information in search engine results or deciding which product to buy in an on-line store. Recommender systems are electronic applications, the aim of which is to support humans in this decision making process. They are widely used in many applications: adaptive WWW servers, e-learning, music and video preferences, internet stores etc. In on-line solutions, such as e-shops or libraries, the aim of recommendations is to show customers the products which they are probably interested in. As input data the following are taken: shopping basket archives, ratings of the products or servers log files.The article presents a solution of recommender system which helps users to select an interesting product. The system analyses data from other customers' ratings of the products. It uses clustering methods to find similarities among the users and proposed techniques to identify users' profiles. The system was implemented in Apache Mahout environment and tested on a movie database. Selected similarity measures are based on: Euclidean distance, cosine as well as correlation coefficient and loglikehood function.

A New Profile Learning Model for Recommendation System based on Machine Learning Technique

“…An approach to recommender systems based on clustering methods is introduced [6]. The clustering part identifies similar users, who then are taken to create clusters profiles.…”

“…Each cluster is a collection of data objects that are similar to one another and dissimilar to the data objects in other cluster [6].…”

“…Unfortunately, these techniques suffer from vital drawbacks. In clustering techniques [6,7], they suffer from low precision, over fitting the training data, time consuming and the difficulty for evaluate a number of clusters automatically as in. In Genetic Algorithms [8,9], the computational requirements and high runtime were their greatest weakness.…”

“…The application of Machine Learning techniques is a standard way to perform the task of learning user profiles in recommender systems [5], such as Clustering [6,7], Genetic Algorithms [8,9], Neural Networks [10,11], and Classification Techniques [12][13][14][15]. Unfortunately, these techniques suffer from vital drawbacks.…”

A Modified Clustering Algorithm DBSCAN Used in a Collaborative Filtering Recommender System for Music Recommendation

“…The set of clusters was generated by modified DBSCAN algorithm with different values of their input parameters and evaluated with respect to their genre homogeneity. Finally, quality of prediction (RMSE) and time efficiency of examined system was calculated and compared to other recommenders: memory-based CF, a recommender system based on k-means clusters [8], SVD model-based approach [19], SlopeOne [9].…”