Collaborative filtering: Techniques and applications

Mustafa, Najdt; Ibrahim, Ashraf Osman; Ahmed, Ali; Abdullah, Afnizanfaizal

doi:10.1109/iccccee.2017.7867668

Cited by 36 publications

(24 citation statements)

References 27 publications

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“…The recommended items are identical to the things that were liked by the customer earlier or previously and they also match the attributes of the user. This technique works well only when the attributes are presented in a clear and proper way [5]. In content based filtering technique the item description and user's profile have a lot of importance.…”

Section: B Content Based Filteringmentioning

confidence: 99%

“…When the profile of the user and the item's attributes are known then the main role of CBF is to foresee whether the active user would like the recommended item or dislike the item [5].…”

Section: Fig 2: Content Based Filteringmentioning

confidence: 99%

“…2. Content based filtering suffers from the overspecialization problem as it recommends items that are all of same type [5]. The user won't get the items beyond the predefined boundary which leads to poor recommendation [1].…”

Section: Limitations Of Content Based Filteringmentioning

confidence: 99%

“…To predict the ratings of the unrated items can be done by using the data mining techniques [3]. In case of this technique on the basis of the information collected from the repository is used to generate a model for recommending without making the use of dataset everytime [5]. Speed and scalability are the two factors that are improved when this approach is used.…”

Section: Model-basedmentioning

confidence: 99%

“…This technique finds the users that resembles the target user's liking and disliking and then predicts the liking of the target user for new items. It is an effective and easy method to implement [5]. Each user having similar taste are grouped together [6].…”

Section: Memory-basedmentioning

confidence: 99%

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Techniques of Recommender System

2019

IJITEE

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The term Recommender system is described as any organization that provides personalized suggestions as a result and it effects the user in the individualized way to favorable items from the large number of opinions. The voluminous inflation of the reachable data online and also the number of users have lead to the information overload problem. To overcome this problem the recommender system came into play as it is able to prioritize and personalize the data. Recommendation systems have developed alongside with the net. Recommender system has mainly three data filtering methods such as content based filtering technique, collaborative based filtering technique and the hybrid approach to manage the data overload problem and to recommends the items to the user the items they are interested in from the dynamically generated data. This paper makes a comprehensive introduction to the recommender system with its types, content based filtering , collaborative filtering and the hybrid recommendation.

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Section: B Content Based Filteringmentioning

confidence: 99%

“…When the profile of the user and the item's attributes are known then the main role of CBF is to foresee whether the active user would like the recommended item or dislike the item [5].…”

Section: Fig 2: Content Based Filteringmentioning

confidence: 99%

Section: Limitations Of Content Based Filteringmentioning

confidence: 99%

Section: Model-basedmentioning

confidence: 99%

Section: Memory-basedmentioning

confidence: 99%

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Techniques of Recommender System

2019

IJITEE

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Retracted: A novel optimized Bat Extreme Learning intrusion detection system for smart Internet of Things networks

Balasundaram

Pushpalatha

2021

Int J Communication

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Summary Rapid growth in technology has triggered the exponential growth in an advancement of the profoundly associated ubiquitous biological system of unavoidable gadgets, all things considered, called as Internet of Things (IoT). Although these devices make everyone's life as flexible and comfortable, they also suffer from various security breaches. Current scenario of security systems which are employed to safeguard the IoT devices are still in darker side, which has been considered as attractive prey for the attackers. This paper proposes the novel BEL three‐tier intrusion detection architecture which is based novel bat optimized extreme learning machines to detect the various cyber‐attacks over the IoT network. The proposed system consists of (1) design of scalable IoT deployable software which is used to collect the data which are used to classify the different behaviors of IoT networks and (2) data analyzer mechanism (DAM) which analyzes the various malicious characteristics of IoT networks after incurring the attacks in the networks and classifies the type of attacks that has been injected. The real‐time scenario of smart IoT networks for smart automation has been created using API NDNOMNeT which is an extension of OMNeT++ in which the 100 IoT devices has been deployed and attack methodology has been created by Python API. The efficiency of the projected IDS planning has been evaluated on the above test beds, deploying different attacks categories such as denial of services (DoS), man in middle (MIM)/spoofing, replay, and data leakage. The experiments demonstrate that the performance of the proposed IDS architecture has outperformed the other existing learning‐based IDS in terms of accuracy, sensitivity, time complexity, and reliability.

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A Novel Movie Recommendation System Based on Collaborative Filtering and Neural Networks

Lin

Chi

2019

Advanced Information Networking and Applications

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Collaborative filtering: Techniques and applications

Cited by 36 publications

References 27 publications

Techniques of Recommender System

Techniques of Recommender System

Retracted: A novel optimized Bat Extreme Learning intrusion detection system for smart Internet of Things networks

A Novel Movie Recommendation System Based on Collaborative Filtering and Neural Networks

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