Evaluating Collaborative Filtering Recommender Algorithms: A Survey

Jalili, Mahdi; Ahmadian, Sajad; Izadi, Maliheh; Moradi, Parham; Salehi, Mostafa

doi:10.1109/access.2018.2883742

Cited by 150 publications

(82 citation statements)

References 116 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This algorithm is divided into user based and item-based algorithms based on the using the target user or target item/product. The user based and item-based methods were proposed to address the issue of scalability in the system [12].…”

Section: Memory-based Collaborative Filtering Techniquementioning

confidence: 99%

“…Various combinations of content based and collaborative filtering techniques exist that can be used to exploit user r item information, ratings and similarities of various users and items. The four ways of combining content based and collaborative filtering techniques includes: separate implementation of content based and collaborative filtering and them merging the result, Boosting the collaborative filtering algorithm using some features of the content based method, boosting the content based method using some characteristics of the collaborative filtering method and unify the collaborative filtering and the content based method into a single recommender system [12].…”

Section: Hybrid Recommendation Techniquementioning

confidence: 99%

See 1 more Smart Citation

Exploration of Collaboration Filtering Techniques for Product Recommendation

Kumar,

Madhavan

2020

IJEAT

View full text Add to dashboard Cite

Today, recommendation system has been globally adopted as the most effective and reliable search engine for knowledge extraction in the field of education, economics and scientific research. Collaborative filtering is a proven techniques used in recommender system to make predictions or recommendations of the unknown preferences for users based on the known user preferences. In this paper, collaborative filtering task and their challenges are explored, study the different recommendation techniques and evaluate their performance using different metrics.

show abstract

Section: Memory-based Collaborative Filtering Techniquementioning

confidence: 99%

Section: Hybrid Recommendation Techniquementioning

confidence: 99%

Exploration of Collaboration Filtering Techniques for Product Recommendation

Kumar,

Madhavan

2020

IJEAT

View full text Add to dashboard Cite

show abstract

“…Joan Borràset al surveyed tourism recommender system [13] and Ruihui Mudiscussed about deep learning based [14] recommender system. Mahdi Jaliliet al reviewedevaluation [15] of collaborative filtering based algorithms. However in view of exploring current state-of-the-art solution in MRS there is a need of review on approaches to MRS.…”

Section: Thisinformationmentioning

confidence: 99%

“…In this model similarity between movies are calculated based on a collaborative behavior [17]. CF based model [15] can be categorized into memory-based and model-based model. Neighborhood based model is generally treated as memory based model.…”

Section: Collaborative Filtering (Cf) Modelmentioning

confidence: 99%

A Research on Collaborative Filtering Based Movie Recommendations: From Neighborhood to Deep Learning Based System

Behera*,

Das,

Swetanisha

2019

IJRTE

View full text Add to dashboard Cite

Recommender System or Recommendation Engine gaining popularity as it can tackle information overload problem. Initially it was considered as a domain of Information Retrieval system and was limited to few applications. With the advancement of different state-of-the-art modeling approaches recommender system can be applicable to many application domains. Movie Recommender System (MRS) is widely explored domain and used by many streaming service providers like Netflix, Amazon Prime, YouTube and many more. This system makes use of users’ data to explore and recommends personally as per their taste. In this paper a detailed study on recently published article related to movie recommendation is carried out. Popular techniques for MRS are commonlycategorized into collaborative filtering, content-based and hybridmethod. Neighborhood-based, latent factor based, neural network based and deep learning based techniques have been continuously evolved with application to MRS. Recently proposed models have been reviewed and it is found that hybrid method performs better as compared to individual model.

show abstract

“…The increasingly importance, use and popularity of recommender systems research both in academia and in industry has led to the development of new algorithms and their experimental evaluation. Researchers are mostly focusing in creating more effective algorithms and models by trying to minimize the MAE and RMSE while at the same time they are trying to improve precision and recall of top-N recommendations [9,10]. While this is important to do, it should be also noted that the problem of reproducing the results exists and it is considered important [11].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Data Set Optimization in Recommender Systems Using Fuzzy T-Norms

Papaleonidas

Pimenidis

Iliadis

2019

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

A recommender system uses specific algorithms and techniques in order to suggest specific services, goods or other type of recommendations that users could be interested in. User's preferences or ratings are used as inputs and top-N recommendations are produced by the system. The evaluation of the recommendations is usually based on accuracy metrics such as the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE), while on the other hand Precision and Recall is used to measure the quality of the top-N recommendations. Recommender systems development has been mainly focused in the development of new recommendation algorithms. However, one of the major problems in modern offline recommendation system is the sparsity of the datasets and the selection of the suitable users Y that could produce the best recommendations for users X. In this paper, we propose an algorithm that uses Fuzzy sets and Fuzzy norms in order to evaluate the correlation between users in the data set so the system can select and use only the most relevant users. At the same time, we are extending our previous work about Reproduction of experiments in recommender systems by developing new explanations and variables for the proposed new algorithm. Our proposed approach has been experimentally evaluated using a real dataset and the results show that it is really efficient and it can increase both accuracy and quality of recommendations.

show abstract

Evaluating Collaborative Filtering Recommender Algorithms: A Survey

Cited by 150 publications

References 116 publications

Exploration of Collaboration Filtering Techniques for Product Recommendation

Exploration of Collaboration Filtering Techniques for Product Recommendation

A Research on Collaborative Filtering Based Movie Recommendations: From Neighborhood to Deep Learning Based System

Hybrid Data Set Optimization in Recommender Systems Using Fuzzy T-Norms

Contact Info

Product

Resources

About