Analisis dan Implementasi Class-Based Collaborative Filtering pada Recommender system

ABSTRAKSI: Recommender system adalah sistem yang dapat digunakan untuk memprediksi sebuah items dalam hal ini berupa movies, berdasarkan informasi yang diperoleh dari user, sehingga didapatkan rekomendasi berdasarkan profil penggunanya. Collaborative filtering merupakan pendekatan pada recommender system yang merekomendasikan items dengan mencari similarity antara user atau antara item berdasarkan informasi yang sudah ada pada user atau item lainnya.Tugas Akhir ini mengimplementasikan dan menganalisis performansi classbased collaborative filtering pada recommender system. Algoritma class-based merupakan pengembangan dari user-based collaborative filtering. Algoritma class-based dalam memprediksi nilai rating suatu item dengan menggabungkan dua konsep yaitu matrix user-class dan instance selection. Sehingga hasil prediksi items yang akan didapatkan menjadi maksimal. Data yang digunakan adalah data set movielens. Faktor-faktor yang digunakan dalam analisis adalah user frequency threshold, given k, dan Ncommon. Tugas akhir ini menganalisis tingkat akurasi prediksi rating yang dihasilkan dengan metoda evaluasi MAE (Mean Absolut Error).Faktor-faktor seperti user frequency threshold, given k, dan Ncommon mempengaruhi tingkat akurasi prediksi rating dinilai dari MAE-nya. Penerapan user frequency threshold dan given k pada algoritma class-based akan cenderung berdampak lebih baik MAE-nya daripada yang tidak diterapkan didalamnya.Kata Kunci : recommender system, collaborative filtering, class-based, instanceABSTRACT: Recommender system is a system that can be used to predict a items in this case in the form of movies, based on the information obtained from the user, so that the recommendations based on the user\u27s profile was obtained. Collaborative filtering in recommender system approach is the recommended items by finding similarity between users or between items on the basis of the information already exists on the user or other items.This final task implements and analyzes the performance of class-based collaborative filtering in recommender system. Class-based algorithms is the development of user-based collaborative filtering. Class-based algorithms to predict the rating an item value by combining two concepts, namely matrix userclass and instance selection. So the prediction results obtained shall become items maximum. Data used is data set movielens. Factors that are used in the analysis are the user frequency threshold, given k and Ncommon. The final task is to analyze the level of accuracy prediction rating produced by the method of evaluation of MAE (Mean Absolute Error).Factors such as user frequency threshold, the given k, and Ncommon affecting the level of accuracy prediction rating based its MAE. The application of user frequency threshold and the given k on algorithms class-based tend impact better of the MAE than not applied it.Keyword: recommender system, collaborative filtering, class-based, instanc

Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity. Nevertheless, there exist multiple relations between items in real-world scenarios. Distinct from the collaborative similarity that implies co-interact patterns from the user perspective, these relations reveal fine-grained knowledge on items from different perspectives of meta-data, functionality, etc. However, how to incorporate multiple item relations is less explored in recommendation research. In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system. We find that both the relation type and the relation value are crucial in inferring user preference. To this end, we develop a two-level hierarchical attention mechanism to model user preference. The first-level attention discriminates which types of relations are more important, and the second-level attention considers the specific relation values to estimate the contribution of a historical item in recommending the target item. To make the item embeddings be reflective of the relational structure between items, we further formulate a task to preserve the item relations, and jointly train it with the recommendation task of preference modeling. Empirical results on two real datasets demonstrate the strong performance of RCF. Furthermore, we also conduct qualitative analyses to show the benefits of explanations brought by the modeling of multiple item relations

Trust-Networks in Recommender Systems

Similarity-based recommender systems suffer from significant limitations, such as data sparseness and scalability. The goal of this research is to improve recommender systems by incorporating the social concepts of trust and reputation. By introducing a trust model we can improve the quality and accuracy of the recommended items. Three trust-based recommendation strategies are presented and evaluated against the popular MovieLens [8] dataset

A Hybrid Recommender Strategy on an Expanded Content Manager in Formal Learning

The main topic of this paper is to find ways to improve learning in a formal Higher Education Area. In this environment, the teacher publishes or suggests contents that support learners in a given course, as supplement of classroom training. Generally, these materials are pre-stored and not changeable. These contents are typically published in learning management systems (the Moodle platform emerges as one of the main choices) or in sites created and maintained on the web by teachers themselves. These scenarios typically include a specific group of students (class) and a given period of time (semester or school year). Contents reutilization often needs replication and its update requires new edition and new submission by teachers. Normally, these systems do not allow learners to add new materials, or to edit existing ones. The paper presents our motivations, and some related concepts and works. We describe the concepts of sequencing and navigation in adaptive learning systems, followed by a short presentation of some of these systems. We then discuss the effects of social interaction on the learners’ choices. Finally, we refer some more related recommender systems and their applicability in supporting learning. One central idea from our proposal is that we believe that students with the same goals and with similar formal study time can benefit from contents' assessments made by learners that already have completed the same courses and have studied the same contents. We present a model for personalized recommendation of learning activities to learners in a formal learning context that considers two systems. In the extended content management system, learners can add new materials, select materials from teachers and from other learners, evaluate and define the time spent studying them. Based on learner profiles and a hybrid recommendation strategy, combining conditional and collaborative filtering, our second system will predict learning activities scores and offers adaptive and suitable sequencing learning contents to learners. We propose that similarities between learners can be based on their evaluation interests and their recent learning history. The recommender support subsystem aims to assist learners at each step suggesting one suitable ordered list of LOs, by decreasing order of relevance. The proposed model has been implemented in the Moodle Learning Management System (LMS), and we present the system’s architecture and design. We will evaluate it in a real higher education formal course and we intend to present experimental results in the near future