An Item-based collaborative filtering method using Item-based hybrid similarity

Puntheeranurak, Sutheera; Chaiwitooanukool, Thanut

doi:10.1109/icsess.2011.5982355

Cited by 18 publications

(12 citation statements)

References 13 publications

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“…In this system it is also possible to use the recommendations of the two filtering techniques independently [9]. A hybrid recommender system is another category of recommender systems that tries to rise above the limitation of other approaches.…”

Section: Hybrid Recommender Systemsmentioning

confidence: 99%

“…This is a method of making automatic predictions about the interests of a user by accumulating preferences from many users [9].The purpose of collaborative filtering systems is to recommend new items to the user or envisage the utility of a certain item, based on user's previous likings and also on the opinions of other likeminded users.…”

Section: Collaborative Filtering Recommender Systemsmentioning

confidence: 99%

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Recommendation System: State of the Art Approach

Aamir¹,

Bhusry²

2015

IJCA

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A Recommender System (RS) is a composition of software tools and machine learning techniques that provides valuable piece of advice for items or services chosen by a user. Recommender systems are currently useful in both the research and in the commercial areas. Numerous approaches have been proposed for providing recommendations. Certainly, recommendation systems have an assortment of properties that may entail experiences of user such as user preference, prediction accuracy, confidence, trust, etc. In this paper we present a categorical reassess of the field of recommender systems and Approaches for Evaluation of Recommendation System to propose the recommendation method that would further help to enhance opinion mining through recommendations.

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Section: Hybrid Recommender Systemsmentioning

confidence: 99%

Section: Collaborative Filtering Recommender Systemsmentioning

confidence: 99%

Recommendation System: State of the Art Approach

Aamir¹,

Bhusry²

2015

IJCA

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“…Puntheeranurak and Chaiwitooanukool [7] proposed innovative algorithm that improves efficiency for items based collaborative filtering technique. In the paper 3 methods are used out of which two already exist and one is derived from them.…”

Section: Literature Reviewmentioning

confidence: 99%

“…E commerce is an emerging field from last few decades.E commerce websites needs different technologies to refer their product to the end user; for example: food, clothing, gadgets, movies, books etc. [7] Recommendation system is one of the technology for the referral purpose to satisfy the customer need. These recommendation systems have a wide area of success in the personalized recommendation to the end user.…”

Section: Introductionmentioning

confidence: 99%

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Design and Implementation of Collaborative Filtering Approach for Movie Recommendation System

Sang¹,

Vishwakarma²

2017

IJCA

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Recommendation systems play a significant role in the user life that provides information filtering from enormous data to the user specific data for decision making. Recommendation system mainly deals with the similarity among objects (items). The collaborative filtering is a recommendation technique that contains a list of rating that the previous user has already given for an item. Using the rating and similarity among the two users, the system recommends an item to the user for the decision making. In the system, separate the movies data set into unrated and rated sample set. Then rated movies dataset is treated as training data with the help of "Item K-NN", builds a model. The model is applied to the unrated movies dataset which is treated as testing data set. At last the order list of unrated movies is created on the basis of distance Pearson and Cosine formula.

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YourMOOC4all: A Recommender System for MOOCs Based on Collaborative Filtering Implementing UDL

Iniesto

Rodrigo

2019

Lecture Notes in Computer Science

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YourMOOC4all is a pilot research project to collect feedback requests regarding accessible design for Massive Open Online Courses (MOOCs). In this online application, a specific website offers the possibility for any learner to freely judge if a particular MOOC complies Universal Design for Learning (UDL) principles. User feedback is of great value for the future development of MOOC platforms and MOOC educational resources, as it will help to follow Design for All guidelines. YourMOOC4all is a recommender system which gathers valuable information directly from learners to improve aspects such as the quality, accessibility and usability of this online learning environment. The final objective of collecting user's feedback is to advice MOOC providers about the missing means for meeting learner needs. This paper describes the pedagogical and technological background of YourMOOC4all and its use cases.

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An Item-based collaborative filtering method using Item-based hybrid similarity

Cited by 18 publications

References 13 publications

Recommendation System: State of the Art Approach

Recommendation System: State of the Art Approach

Design and Implementation of Collaborative Filtering Approach for Movie Recommendation System

YourMOOC4all: A Recommender System for MOOCs Based on Collaborative Filtering Implementing UDL

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