scholarly journals Assessment Methods for Evaluation of Recommender Systems: A Survey

2021 ◽  
Vol 46 (4) ◽  
pp. 393-421
Author(s):  
Madhusree Kuanr ◽  
Puspanjali Mohapatra
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Information Filtering ◽  
Assessment Methods ◽  
Past Behavior ◽  
Large Pool ◽  
Near Future ◽  
The Relationship

Abstract The recommender system (RS) filters out important information from a large pool of dynamically generated information to set some important decisions in terms of some recommendations according to the user’s past behavior, preferences, and interests. A recommender system is the subclass of information filtering systems that can anticipate the needs of the user before the needs are recognized by the user in the near future. But an evaluation of the recommender system is an important factor as it involves the trust of the user in the system. Various incompatible assessment methods are used for the evaluation of recommender systems, but the proper evaluation of a recommender system needs a particular objective set by the recommender system. This paper surveys and organizes the concepts and definitions of various metrics to assess recommender systems. Also, this survey tries to find out the relationship between the assessment methods and their categorization by type.

scholarly journals Crop Recommender System for the Farmers using Mamdani Fuzzy Inference Model

2018 ◽  
Vol 7 (4.15) ◽  
pp. 277 ◽  
Author(s):  
Madhusree Kuanr ◽  
Bikram Kesari Rath ◽  
Sachi Nandan Mohanty
Keyword(s):  
Fuzzy Logic ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Fuzzy Inference ◽  
Weather Condition ◽  
Cosine Similarity ◽  
Inference Model ◽  
Large Pool ◽  
Cosine Similarity Measure

Recommender systems provide suggestions to the users for choosing particular items from a large pool of items. The purpose of this study is to design a collaborative recommender system for the farmers for recommending giving prior idea regarding a crop which is suitable according to the location of the farmer based on weather condition of the previous months. The proposed system also recommends other seeds, pesticides and instruments according to the preferences in farming and location of the farmers while purchasing the seeds through online. It uses cosine similarity measure to find the similar user according the location of the farmer and fuzzy logic for predicting the yield of rice crop for Kharif season in state Odisha, India. The proposed system is implemented in Mamdani Fuzzy Inference model. The results reveal that it provides prior idea regarding a crop before sowing of seeds.  

scholarly journals Community Detection-Based Recommendation Framework

2018 ◽  
pp. 61-66
Author(s):  
Karen Mkhitaryan
Keyword(s):  
Computational Complexity ◽  
Recommender Systems ◽  
Community Detection ◽  
Recommender System ◽  
Information Filtering ◽  
Detection Methods

Recommender system is a type of information filtering system predicting users preferences about items, aiming to generate personalized recommendations. Various recommendation approaches exist in the literature that differ in terms of methodology and types of systems they can be utilized on. In recent years some attempts have been made to incorporate community detection methods into recommender systems to make the process of recommendation generation more accurate in terms of rating or preference prediction and efficient in terms of computational complexity. In this paper we propose a community detection-based approach for recommender system, which is more reasonable in certain applications.

Recommending Academic Papers for Learning Based on Information Filtering Applied to Mobile Environments

2015 ◽  
pp. 2159-2178
Author(s):  
Sílvio César Cazella ◽  
Jorge Luiz Victória Barbosa ◽  
Eliseo Berni Reategui ◽  
Patricia Alejandra Behar ◽  
Otavio Costa Acosta
Keyword(s):  
Learning Environment ◽  
Recommender Systems ◽  
Mobile Devices ◽  
Mobile Learning ◽  
Recommender System ◽  
Information Filtering ◽  
Likert Scale ◽  
Learning Objects ◽  
Mobile Environments ◽  
Proposed Model

Mobile learning is about increasing learners' capability to carry their own learning environment along with them. Recommender Systems are widely used nowadays, especially in e-commerce sites and mobile devices, for example, Amazon.com and Submarino.com. In this chapter, the authors propose the use of such systems in the area of education, specifically for the recommendation of learning objects in mobile devices. The advantage of using Recommender Systems in mobile devices is that it is an easy way to deliver recommendations to students. Based on this scenario, this chapter presents a model of a recommender system based on information filtering for mobile environments. The proposed model was implemented in a prototype aimed to recommend learning objects in mobile devices. The evaluation of the received recommendations was conducted using a Likert scale of 5 points. At the end of this chapter, some future works are described.

Challenges and Applications of Recommender Systems in E-Commerce

2021 ◽  
pp. 175-188
Author(s):  
Taushif Anwar ◽  
V. Uma ◽  
Md Imran Hussain
Keyword(s):  
Recommender Systems ◽  
User Experience ◽  
Recommender System ◽  
Recommendation System ◽  
Main Idea ◽  
Important Task ◽  
Significant Challenge ◽  
Online Business ◽  
The One ◽  
The Relationship

E-commerce and online business are getting too much attention and popularity in this era. A significant challenge is helping a customer through the recommendation of a big list of items to find the one they will like the most efficiently. The most important task of a recommendation system is to improve user experience through the most relevant recommendation of items based on their past behaviour. In e-commerce, the main idea behind the recommender system is to establish the relationship between users and items to recommend the most relevant items to the particular user. Most of the e-commerce websites such as Amazon, Flipkart, E-Bay, etc. are already applying the recommender system to assist their users in finding appropriate items. The main objective of this chapter is to illustrate and examine the issues, attacks, and research applications related to the recommender system.

On the integration of Machine Learning algorithms and operations research techniques in the development of a hybrid Recommender System

2021 ◽  
pp. 1-14
Author(s):  
Panagiotis Giannopoulos ◽  
Georgios Kournetas ◽  
Nikos Karacapilidis
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Learning Algorithms ◽  
Information Filtering ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbors ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Recommender Systems is a highly applicable subclass of information filtering systems, aiming to provide users with personalized item suggestions. These systems build on collaborative filtering and content-based methods to overcome the information overload issue. Hybrid recommender systems combine the abovementioned methods and are generally proved to be more efficient than the classical approaches. In this paper, we propose a novel approach for the development of a hybrid recommender system that is able to make recommendations under the limitation of processing small amounts of data with strong intercorrelation. The proposed hybrid solution integrates Machine Learning and Multi-Criteria Decision Analysis algorithms. The experimental evaluation of the proposed solution indicates that it performs better than widely used Machine Learning algorithms such as the k-Nearest Neighbors and Decision Trees.

Recommending Academic Papers for Learning Based on Information Filtering Applied to Mobile Environments

2014 ◽  
pp. 120-140 ◽  
Author(s):  
Sílvio César Cazella ◽  
Jorge Luiz Victória Barbosa ◽  
Eliseo Berni Reategui ◽  
Patricia Alejandra Behar ◽  
Otavio Costa Acosta
Keyword(s):  
Learning Environment ◽  
Recommender Systems ◽  
Mobile Devices ◽  
Mobile Learning ◽  
Recommender System ◽  
Information Filtering ◽  
Likert Scale ◽  
Learning Objects ◽  
Mobile Environments ◽  
Proposed Model

Mobile learning is about increasing learners' capability to carry their own learning environment along with them. Recommender Systems are widely used nowadays, especially in e-commerce sites and mobile devices, for example, Amazon.com and Submarino.com. In this chapter, the authors propose the use of such systems in the area of education, specifically for the recommendation of learning objects in mobile devices. The advantage of using Recommender Systems in mobile devices is that it is an easy way to deliver recommendations to students. Based on this scenario, this chapter presents a model of a recommender system based on information filtering for mobile environments. The proposed model was implemented in a prototype aimed to recommend learning objects in mobile devices. The evaluation of the received recommendations was conducted using a Likert scale of 5 points. At the end of this chapter, some future works are described.

scholarly journals Customer Loyalty Improves the Effectiveness of Recommender Systems Based on Complex Network

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 171 ◽  
Author(s):  
Yun Bai ◽  
Suling Jia ◽  
Shuangzhe Wang ◽  
Binkai Tan
Keyword(s):  
Random Walk ◽  
Recommender Systems ◽  
Network Topology ◽  
Customer Loyalty ◽  
Recommender System ◽  
Recommendation Algorithm ◽  
Single Vertex ◽  
Vertex Set ◽  
Product Recommendations ◽  
The Relationship

Inferring customers’ preferences and recommending suitable products is a challenging task for companies, although recommender systems are constantly evolving. Loyalty is an indicator that measures the preference relationship between customers and products in the field of marketing. To this end, the aim of this study is to explore whether customer loyalty can improve the accuracy of the recommender system. Two algorithms based on complex networks are proposed: a recommendation algorithm based on bipartite graph and PersonalRank (BGPR), and a recommendation algorithm based on single vertex set network and DeepWalk (SVDW). In both algorithms, loyalty is taken as an attribute of the customer, and the relationship between customers and products is abstracted into the network topology. During the random walk among nodes in the network, product recommendations for customers are completed. Taking a real estate group in Malaysia as an example, the experimental results verify that customer loyalty can indeed improve the accuracy of the recommender system. We can also conclude that companies are more effective at recommending customers with moderate loyalty levels.

A Recommendation Method Based on Semantic Similarity and Complementarity Using Weighted Taxonomy: A Case on Construction Materials Dataset

2018 ◽  
Vol 17 (01) ◽  
pp. 1850010
Author(s):  
Karamollah Bagherifard ◽  
Mohsen Rahmani ◽  
Vahid Rafe ◽  
Mehrbakhsh Nilashi
Keyword(s):  
Data Mining ◽  
Recommender Systems ◽  
Semantic Similarity ◽  
Recommender System ◽  
Construction Materials ◽  
Similar Degree ◽  
Web Pages ◽  
New Approach ◽  
Main Components ◽  
The Relationship

Products and web pages are the main components of the e-commerce data knowledge and the relationship among them is an important issue to be highly considered in recommender systems. This study aims to focus on the similarity and complementarity relationships among the products that have wide applications in the recommender systems. In the previously proposed methods, products and their relationships were revealed using taxonomy and “IS-A” relationship. In addition, the similarity and complementarity calculations were conducted based on edge computation by assigning a similar degree to any edge. More specifically, the children of a concept in the taxonomy was supported by a similar father’s “IS-A” degree. In contrast, this study provides a new approach based on ontology, data mining, and automatic discovering algorithms for the relationships with different degrees for the edges among the concepts. Accordingly, these relationships are initialised according to the “IS-A” degree. With regard to this weighted taxonomy, the semantic similarity and complementarity are measured based on concept distance. In addition, the proposed recommender system is item-based, which uses semantic similarity and complementarity. The required data for the present study were collected from construction materials supplier. The results illustrated that our proposed method is effective for construction materials recommendation.

scholarly journals Who Likes What Kind of News? The Relationship Between Characteristics of Media Consumers and News Interest

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402110030
Author(s):  
Kai Kaspar ◽  
Lisa Anna Marie Fuchs
Keyword(s):  
Big Five ◽  
Information Filtering ◽  
Personal Characteristics ◽  
Self Esteem ◽  
Big Five Personality ◽  
Regression Analyses ◽  
Uses And Gratification ◽  
German Speaking ◽  
Explained Variance ◽  
The Relationship

Stimulated by the uses-and-gratification approach, this study examined the joint relation of several consumer characteristics to news interest. In total, 1,546 German-speaking participants rated their interest in 15 major news categories and several personal characteristics, including gender, age, the Big Five personality traits, self-esteem, as well as general positive and negative affect. Regression analyses examined the amount of interindividual variance in news interest that can be explained by this set of consumer characteristics. Overall, the amount of explained variance differed remarkably across news categories, ranging from 4% for entertainment-related news to 25% for news about technology. The most powerful explaining variables were participants’ gender, age, openness to experiences, and their amount of general positive affect. The results suggest that news interest should be defined and operationalized as a concept with multiple facets covering a huge range of content. Also, the results are important for media producers and journalists with respect to the conflict between increased need gratification of consumers and information filtering via personalized news content.

scholarly journals Recommender systems in model-driven engineering

2021 ◽  
Author(s):  
Lissette Almonte ◽  
Esther Guerra ◽  
Iván Cantador ◽  
Juan de Lara
Keyword(s):  
Recommender Systems ◽  
Information Filtering ◽  
Research Work ◽  
Model Driven Engineering ◽  
Video Broadcasting ◽  
Model Driven ◽  
Challenges And Opportunities ◽  
Mapping Review ◽  
Development Processes ◽  
Systematic Mapping Review

AbstractRecommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research.

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