A Multi -Perspective Evaluation of MA and GA for Collaborative Filtering Recommender System

Banati, Hema; Mehta, Shikha

doi:10.5121/ijcsit.2010.2508

Cited by 11 publications

(3 citation statements)

References 54 publications

(38 reference statements)

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“…This model uses k-means clustering for user grouping and cuckoo search for the process of prediction. Other metaheuristic or evolutionary algorithm based recommendation systems include memetic algorithm and genetic algorithm based recommender system by Banati et al [17], PSO based recommender system [18] and fuzzy ant based recommenders by Nadi et al [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Augmentation based Hybrid Collaborative Filtering using Tree Boosted Ensemble

Balasingam

Palaniswamy

2019

IJCAI

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Requirements for recommendation systems are currently on the raise due to the huge information content available online and the inability of users to manually filter required data. This paper proposes a Feature augmentation based hybrid collaborative filtering using Tree Boosted Ensemble (TBE), for prediction. The proposed TBE recommender is formulated in two phases. The first phase creates category based training matrix using similar user profiles, while the second phase employs the boosted tree based model to predict ratings for the items. A threshold based filtering is finally applied to obtain precise recommendations for the user. Experiments were conducted with MovieLens dataset and performances were measured in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The proposed model was observed to exhibit MAE levels of 0.64 and RMSE levels of 0.77 with a variation level of ±0.1. Comparisons with state-of-the-art models indicate that the proposed TBE model exhibits reductions in MAE at 6% to 14% and RMSE at ~0.2. Povzetek: Avtorji so razvili novo metodo za svetovalne sisteme, temelječo na bogatitvi atributov za filtriranje s pomočjo spodbujevalnega ansambla dreves (TBE).

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Section: Literature Reviewmentioning

confidence: 99%

Feature Augmentation based Hybrid Collaborative Filtering using Tree Boosted Ensemble

Balasingam

Palaniswamy

2019

IJCAI

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“…Such as, Li G. and Li L. (2012) [2], Shani G., D. Heckerman, and R. I. Brafman (2005) [3], Banati H. and Mehta S. (2010) [4]. On the basis of different technologies, CF system is mainly divided into two categories, one is based on storage, and the other is model.…”

Section: Collaborative Filtering (Cf)mentioning

confidence: 99%

“…We learned something from those few literatures, for example, Yang S. and Xue W. [4]. As for one class collaborative filtering problems, due to the active dataset rarely (sparsity), while the other two types of datasets, negative and missing datasets, are very confusing, it has many difficulties to future research.…”

Section: One Class Collaborative Filtering (Occf)mentioning

confidence: 99%

A Model for Collaborative Filtering Recommendation in E-Commerce Environment

Jing¹

2013

INT J COMPUT COMMUN

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In modern business environment, product life cycle gets shorter and the customer's buying preference changes over time. Time plays a more and more important role in collaborative filtering. However, there is a gap in one class collaborative filtering (OCCF). On the basis of collecting different real-time information, this paper proposes an optimization model for e-retailers. Through comparing different methods with different weights, results show that real-time dependent in OCCF performs better in improving the quality of recommendation. The model is effective in cross-selling e-commerce, personalized, targeted recommendation sales.

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Differential Evolution in a Recommendation System Based on Collaborative Filtering

Boryczka

Bałchanowski

2016

Lecture Notes in Computer Science

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A Multi -Perspective Evaluation of MA and GA for Collaborative Filtering Recommender System

Abstract: ABSTRACT

Cited by 11 publications

References 54 publications

Feature Augmentation based Hybrid Collaborative Filtering using Tree Boosted Ensemble

Feature Augmentation based Hybrid Collaborative Filtering using Tree Boosted Ensemble

A Model for Collaborative Filtering Recommendation in E-Commerce Environment

Differential Evolution in a Recommendation System Based on Collaborative Filtering

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