A general framework for intelligent recommender systems

Aguilar, José; Valdiviezo-Díaz, Priscila; Riofrio, Guido

doi:10.1016/j.aci.2016.08.002

Cited by 70 publications

(35 citation statements)

References 3 publications

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“…Recommender system assists users in decision making by providing suggestion of items to users [3]. A recommender system overlaps domains such as Artificial intelligence, data and semantics mining, information retrieval.…”

Section: Review Of Related Workmentioning

confidence: 99%

A Model of Intelligent Recommender System with Explicit Feedback Mechanism for Performance Improvement

Awodele¹,

Temitope.²,

Adekunle³

et al. 2020

IJIRCST

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Recommender Systems are intelligent applications designed to assist the user in a decision-making process whereby user wants to choose one item amongst the potentially overwhelming set of alternative products or services. This work focused on using users' bank statements that explicitly shows inflow and outflow of funds. The dataset used is real and reliable because the use of non-reliable data in a recommender system causes users lack of trust in the system. However, the data collected were anonymized for privacy reasons. The recommender system was developed as a web application using Java programming language. Unlike other recommender systems, the graph-oriented database management system was used. In Google news, 38% of the total views are the result of recommendations; similarly, 60% of the rented movies from Netflix come from recommendations and more than that Amazon sales percentage due to recommendations are 35%. Successful integration of recommendation system by online companies like Amazon, eBay, Flipkart amongst others impelled the research community to avail similar benefits in Financial domain to recommend product and services [26]. Therefore, recommendation systems are considered an expedient factor in business nowadays. The aim of all recommender systems is to provide recommendation that will be favourably evaluated and accepted by its users.

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Section: Review Of Related Workmentioning

confidence: 99%

A Model of Intelligent Recommender System with Explicit Feedback Mechanism for Performance Improvement

Awodele¹,

Temitope.²,

Adekunle³

et al. 2020

IJIRCST

View full text Add to dashboard Cite

show abstract

“…All these sorts of RS make data browsing easier with big data attributes. [18] Developed an outline for intelligent model that is referred by knowledge based structure, learning schemes and intellectual processes and criticality system to extend the notion of knowledge based recommendation method. Apart from these, it includes individuals, products, area, context and criticism as knowledge model.…”

Section: Adapted From [9] Fig3modified Entropy Classifier With Biparmentioning

confidence: 99%

A Systematic Learning on Variety of Recommender Systems for Online Commodities

Daniel¹,

Meena²

2019

IJITEE

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In a sophisticated high-end product market, all firms often come up with a vast number of goods to partake the market shares. Owing to the availability of enough information of various products that enters the market or due to lack of right information, customers are prone to the state of dilemma in comparing and choosing the most appropriate ones. In most of the cases, the product specifications are mentioned, still whether these features suit the customers need is a concern. Online reviews tend to benefit the consumers and the goods developers. Here again, finding out the more supportive reviews become a challenge. Considering these factors, this article intends to be particular in reviewing the existing evaluation strategies and recommender systems that have grown progressively favorable in present era and are employed widely for casual to commercial items

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“…Present work implements one intelligent recommender system based on the Fuzzy Cognitive Maps (FCMs). Next, to evaluate the performance and versatility execution of the intelligent recommender system is tested based on specialized criteria making use of the knowledge [8]. In [9] the Team Recommender Systems (TRS) is proposed.…”

Section: Literature Surveymentioning

confidence: 99%

A Personalized Hybrid Recommendation Procedure for Internet Shopping Support

Shanthi¹,

Rajagopalan²

2018

ijacsa

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Lately, recommender systems (RS) have offered a remarkable breakthrough to users. It lessens the user time cost thereby delivering faster and better results. After purchasing a product there are recommendations according to the different comments provided by users. Within a short span of product utilization and quality, the users receive a product recommendation. But this doesn't work out good so as to make it much better;feedbacks, commands and reviews are fetched on the basis of in-depth commands, globally like and normal keys. Recommendation systems are crucially important for the delivery of personalized product to users. With personalized recommendation to product, users can enjoy a variety of targeted recommendations such as online product; the current paper suggests hybrid recommendation system (HRS) that makes use of rating and review to recommend any product to user. The main objective of this paper is to personalize recommendation of product that have become extremely effective revenue drivers for online shopping business. Despite the great benefits, deploying personalized recommendation services typically requires the collection of users' personal data for processing and analytics, which undesirably makes users. To implement product recommendations following are incorporated that is retrieving personal data, Logical Language based Rule Generation (LLRG), ranking and Hybrid recommendation system. The stages in the suggested recommendation system include, Data Gathering, preprocessing, filtering and Ranking. The Ranking algorithm ranks the products in relation to the sales count. The top list displays the product having greatest count number. In the LLRG strategy, the logic rule generation methodology retrieves useful and mandatory data from reviews, commands, products original state and thereafter comes the recommendation. The HRS enforces two techniques, namely, location based and the other being heterogeneous domain based. Also the recommendations presented to the user are in context to the user's activities, choices and conduct that are in accord with user's personal likings and aids in decision making. When comparing the outcome, it is clear that the suggested method is superior than the traditional with regard to clarity, effective recommendation and coverage rate. It's evaluated that Hybrid Recommendation System yields in greater results compared with rest of the existing recommendation techniques. We, also identity to some future research directions.

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A general framework for intelligent recommender systems

Cited by 70 publications

References 3 publications

A Model of Intelligent Recommender System with Explicit Feedback Mechanism for Performance Improvement

A Model of Intelligent Recommender System with Explicit Feedback Mechanism for Performance Improvement

A Systematic Learning on Variety of Recommender Systems for Online Commodities

A Personalized Hybrid Recommendation Procedure for Internet Shopping Support

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