A Particle Swarm Approach to Collaborative Filtering based Recommender Systems through Fuzzy Features

Wasid, Mohammed; Kant, Vibhor

doi:10.1016/j.procs.2015.06.051

Cited by 48 publications

(13 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to its bio-style optimizing method, it is very computational time expensive. Evolutionary Computing [41,42,44,45] A novel optimizing strategy, sometimes it outperforms conventional weighting strategy. It is able to explore new features in the global area with a smaller training set.…”

Section: Reinforcementmentioning

confidence: 99%

“…Fuzzy Set [34,36,40,41] Shows satisfactory performance in handling the incomplete or imprecise information, which is very significant in modelling a real-life problem.…”

Section: Uncertainty Representation and Modellingmentioning

confidence: 99%

“…A recommending process can be regarded as a process of filtering out irrelevant information based on the user's preference and requirements [41]. The key success of a recommender system depends on the effectiveness and efficiency of the filtering process [42,43].…”

Section: Soft Computing For User Model Constructionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Reinforcementmentioning

confidence: 99%

“…Fuzzy Set [34,36,40,41] Shows satisfactory performance in handling the incomplete or imprecise information, which is very significant in modelling a real-life problem.…”

Section: Uncertainty Representation and Modellingmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In this way, several authors have developed similar researches, such as flexible models of user preference learning from rating values in CBRS, supported by fuzzy sets [50], being some approaches empowered by bioinspired algorithms such as particle swarm optimization [136], to learn user weights on various features. Here it is worthy to note the development of tag-based user profiling methods for improving recommendations [9], where user profiles are built through a folksonomy-based approach that evaluates items according to the membership degrees to various attribute values, which are then used to compute the fuzzy user profile.…”

Section: Proposalsmentioning

confidence: 99%

“…Wasid and Kant [136] Use fuzzy sets for modelling user features, and particle swarm optimization for weighting it.…”

Section: Movielens+hetrec Nomentioning

confidence: 99%

Fuzzy Tools in Recommender Systems: A Survey

Toledo¹,

Martínez²

2017

IJCIS

152

View full text Add to dashboard Cite

Recommender systems are currently successful solutions for facilitating access for online users to the information that fits their preferences and needs in overloaded search spaces. In the last years several methodologies have been developed to improve their performance. This paper is focused on developing a review on the use of fuzzy tools in recommender systems, for detecting the more common research topics and also the research gaps, in order to suggest future research lines for boosting the current developments in fuzzy-based recommender systems. Specifically, it is developed an analysis of the papers focused at such aim, indexed in Thomson Reuters Web of Science database, in terms of they key features, evaluation strategies, datasets employed, and application areas.

show abstract

Multicriteria recommendation based on bacterial foraging optimization

Geng

Wang

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

Recommender systems assist users to make decisions among a huge volume of options. Accuracy-oriented recommender systems focus on the prediction power of algorithms and neglect that users may appreciate diverse and novel recommendations in real-world scenarios. Thus, this paper proposed a multicriteria recommendation model that can optimize the recommendation accuracy, diversity, novelty, and individual tendency simultaneously.Additionally, a new multiobjective bacterial foraging optimization method is proposed to improve its searching capability and the performance of recommendation model. The proposed optimization-based multicriteria recommendation algorithm is compared with existing methods on both benchmark functions and real-world data sets. The results demonstrate that the proposed algorithm is superior to other recommendation algorithms in most cases. This study provides insights in recommendation system design and draws scholarly attention to the optimization-based recommendation strategy.

show abstract

A Particle Swarm Approach to Collaborative Filtering based Recommender Systems through Fuzzy Features

Cited by 48 publications

References 12 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

Fuzzy Tools in Recommender Systems: A Survey

Multicriteria recommendation based on bacterial foraging optimization

Contact Info

Product

Resources

About