Using Genetic Algorithm for Hybrid Modes of Collaborative Filtering in Online Recommenders

Fong, Simon; Ho, Yew Kam; Yang, Hang

doi:10.1109/his.2008.59

Cited by 22 publications

(11 citation statements)

References 7 publications

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“…Object Representation (User / Item Modelling) Swarm Intelligence [40,46] A novel optimizing strategy, sometimes it outperforms conventional weighting strategy. Compared to GA, PSO requires less computational time and more accurate.…”

Section: Reinforcementmentioning

confidence: 99%

From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning

et al. 2019

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The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people's daily activities. Such development together with the lifelong learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners' fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning.

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Section: Reinforcementmentioning

confidence: 99%

From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning

et al. 2019

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“…In these systems, GAs are commonly used to improve the efficiency of the clustering algorithms, for example finding the best suitable initial centers of K-means [6,7]. The hybrid models usually employed the attributes of items or users, such as material of items or demographic information of users [8][9][10][11]. A few papers proposed recommend systems for music using GAs [12][13][14].…”

Section: Related Workmentioning

confidence: 99%

An Item Based Collaborative Filtering System Combined with Genetic Algorithms Using Rating Behavior

Xiao

Luo

Chen

et al. 2015

Lecture Notes in Computer Science

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Abstract. With the sharp increment of information on the Internet, many technologies have been proposed to solve the problem of information explosion in people's life. Collaborative Filtering (CF) recommendation system is one of the most popular and efficient ways of solutions, especially item based CF systems. While traditional item based CF recommendation algorithms either ignore the diversity of different users' rating behavior or do not deal with it efficiently. In this paper, we present a novel similarity function using the average rating for each user instead of the overall average rating for all users. In order to find the optimal similarity function, we use genetic algorithm (GA) to optimize the weight vectors associated to the similarity function. A series of comparison experiments are conducted to demonstrate the effectiveness in terms of the quality of prediction of the proposed method.

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“…According to Simon Fong, Yvonne Ho, Yang Hang when there is a large population of variables to be considered to make recommendations, Genetic Algorithm (GA) [16] search function optimizes the recommendation results which also reduce the Cold-Start Problem. Further the process of coding the variables into GA chromosomes in various mods is explained.…”

Section: B Similar Workmentioning

confidence: 99%

User Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation

Uyangoda

Ahangama

Ranasinghe³

2018

2018 Thirteenth International Conference on Digital Information Management (ICDIM)

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A huge amount of user generated content related to movies is created with the popularization of web 2.0. With these continues exponential growth of data, there is an inevitable need for recommender systems as people find it difficult to make informed and timely decisions. Movie recommendation systems assist users to find the next interest or the best recommendation. In this proposed approach the authors apply the relationship of user feature-scores derived from user-item interaction via ratings to optimize the prediction algorithm's input parameters used in the recommender system to improve the accuracy of predictions when there are less past user records. This addresses a major drawback in collaborative filtering, the cold start problem by showing an improvement of 8.4% compared to the base collaborative filtering algorithm. The user-feature generation and evaluation of the system is carried out using the 'MovieLens 100k dataset'. The proposed system can be generalized to other domains as well.

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Using Genetic Algorithm for Hybrid Modes of Collaborative Filtering in Online Recommenders

Abstract: Abstract

Cited by 22 publications

References 7 publications

From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning

From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning

An Item Based Collaborative Filtering System Combined with Genetic Algorithms Using Rating Behavior

User Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation

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