Recommendation systems: Principles, methods and evaluation

Isinkaye, Folasade Olubusola; Folajimi, Yetunde; Ojokoh, Bolanle Adefowoke

doi:10.1016/j.eij.2015.06.005

Cited by 1,044 publications

(560 citation statements)

References 65 publications

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“…Isinkaye et al [4] explore the different characteristics and potential of the different prediction technique in recommendation technique. In this work, they have further added different phases of recommendation to predict an item.…”

Section: Literature Reviewmentioning

confidence: 99%

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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Section: Literature Reviewmentioning

confidence: 99%

Design and Implementation of Collaborative Filtering Approach for Movie Recommendation System

Sang¹,

Vishwakarma²

2017

IJCA

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“…It is explained as follows 1) Measurement of Trust: Trust-ability is the degree of trust between users. The primary task is to manage quantitatively trust relationship before trust factors introduced to the collaborative filtering recommendation method [4], as thus to use in the calculation formula.…”

Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

Trust Based Novel Recommendation Regularized with Item Ratings

Priyadharshini¹

2017

IJRASET

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Recommendation is an opinion given by an analyst to his/her client whether the given stock is worth buying or a particular place is worth visiting or not. They use various projections as a basis for issuing recommendations. Item rating is a group of classifications designed to extract information about a quantitative or qualitative attribute. Here we use a scale to reflect the quality of product where user selects the number which is taken into consideration. In order to enhance the novel recommendation model, we propose a trust based recommendation model with item rating where data sparsity and cold start problem are rectified.We make use of personalized social networking to connect people in a commodity so that people can get to know about a product or place in detail by the information shared about it and the user can sort out things according to their needs and specification. Keywords: TrustSVD, Itemratings, Filtration,Recommendation,Privacy preservance. 1.INTRODUCTION Recommender systems have been widely used to provide users with high-quality personalized recommendations from a large volume of choices. In order to reduce the data sparsity and cold start problems and their degradation of recommendation performance we use Trust SVD integrates multiple information sources into the recommendation model. An analysis of trust based social network data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Trust SVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further joining both the explicit and implicit influence of trusted[1] and trusting users on the prediction of items for an active user. The proposed technique is to extend SVD++ with social trust information. Experimental results on the four data sets exhibit that Trust SVD achieves better accuracy than other counter parts of recommendation techniques. A. Role of Apache ServerApache Server is one of the popular open source software organizations. It is free and commercial friendly -no licensing fees or costs. It runs pretty much on any OS (Linux, Windows and MacOS). It is maintained regularly with the norms. It is one of the most feature rich web servers available. By nomenclature "Apache" is Apache HTTP Server (sometimes also called Apache httpd -after the name of the process). The other "Apache" server is Apache Tomcat. B. Role of MySQLMySQL is a free, open-source database management system (DBMS for short). A DBMS is a system that manages databases and connects them to server. For example, a MySQL database can be used to run a website, to run the database of an ERP or any other software. II.RELATED WORK In existing trust based social network provides an alternative view of user preferences rather than item ratings. Find that trust social networks are small-world networks where two random users are socially connecte...

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“…Як свідчить проведений аналіз, методи, що найчастіше використовуються при створенні рекомендаційних систем, можна поділити на дві групи: колаборативний (collaborative) та контент-ний (content-based) [11,17,20,23,24]. Крім того, у [17,20,25] розглядається інший метод, що базує-ться на апріорному знанні потреб користувача (knowledge-based), а також його різновид, який відокремився у самостійний метод -рекомендації на основі онтології (ontology-based).…”

Section: дослідження особливостей апаратно-програмних платформunclassified

Research and Practical Implementation of Recommendation System for the Selection of Hardware-Software Platforms for the Embedded Systems Computer Aided Design

Gladkova¹,

Parkhomenko²

2017

Nauk. pr. Donec'k. nac. teh. univ., Ser. Inform. kibern. obcisl

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Recommendation systems: Principles, methods and evaluation

Cited by 1,044 publications

References 65 publications

Design and Implementation of Collaborative Filtering Approach for Movie Recommendation System

Design and Implementation of Collaborative Filtering Approach for Movie Recommendation System

Trust Based Novel Recommendation Regularized with Item Ratings

Research and Practical Implementation of Recommendation System for the Selection of Hardware-Software Platforms for the Embedded Systems Computer Aided Design

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