Recommender Systems Challenges and Solutions Survey

Mohamed, Marwa Hussien; Khafagy, Mohamed Helmy; Ibrahim, Mohamed Hasan

doi:10.1109/itce.2019.8646645

Cited by 102 publications

(32 citation statements)

References 19 publications

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“…These approaches are the CF, Ensemble Divide and Conquer [13], Temporal Dynamics [25], Long Temporal-based Factorization [15], Short Temporal-based Factorization [16], and Temporalbased Factorization Approach [1]. These approaches are implemented using MovieLens dataset underscoring [1][2][3][4][5]. Additionally, Short Temporal [16], and Temporal-based Factorization Approach [1] are implemented for three short terms which are 1 month, 2 weeks, and 1 week.…”

Section: B Evaluation and Benchmark Methodsmentioning

confidence: 99%

“…The major resources (data) used to create recommendations are customer profiles, item profiles, and user-item connections (i.e., customer scores to the suggested items) [2]. Collaborative filtering (CF), contentbased filtering, demographic filtering, and hybrid filtering are four forms of filters employed in recommendation systems [3]. CF is one of the best prevalent recommendation techniques that provide users with personalized predictions based on their preferences.…”

Section: Introductionmentioning

confidence: 99%

“…For example, CF utilizes the rating scores of neighbours to predict a list of items to the active user. However, CF suffers from three main issues: data sparsity [3], [4], cold start [5], [6], and scalability [7] [8]. This paper briefly discusses the approaches utilized to solve the sparsity issue.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Temporal-based Optimization to Solve Data Sparsity in Collaborative Filtering

Al-Hadi¹,

Alomari²,

Alshari³

et al. 2020

IJACSA

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Section: B Evaluation and Benchmark Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Temporal-based Optimization to Solve Data Sparsity in Collaborative Filtering

Al-Hadi¹,

Alomari²,

Alshari³

et al. 2020

IJACSA

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“…As well as the implementation and assessment of interactive filtering algorithms, the analysis highlights basic implications of flexible web users. Once again [15], useful research has been analyzed explaining the use of a contentbased model to optimize existing performed demonstrating the various kinds of methods utilizing collaborative filtering and assessing recommendation mechanisms through prediction accuracy, offline evaluation structure, datasets, accuracy over time, ranking accuracy, online evaluations, decision support metrics etc. Along with these, suggestion framework for technology-enhanced learning has been implemented effectively [12].…”

Section: Related Workmentioning

confidence: 99%

The University Recommendation System for Higher Education

Nalawade¹,

Tiple²

2020

IJRTE

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Recommendation system has become a requirement in today’s world. This recommendation system is spread over large sectors right from education, entertainment, health, business etc. The University Recommendation System is a system which recommend right university for the students based on their GRE, TOFEL score. Due to rapid increase in data and lack of learning in education filed, students often halt selecting wrong universities. Thus, there is a need of recommendation system which is trained to understand the user’s requirements and suggests them that suitable university which they need. Typically, recommendation mechanisms are used based on user’s historical data. It helps the user to discover information and settle on right choices where they do not have the required learning to judge a specific item. This paper explores the flow, types and uses of recommendation system in education.

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“…Recommender system is an effective tool for helping the user in cutting the time needs to find personalised movies, products, documents, friends, places, services, among others [2]. Also, a recommender system is one of the most important and new research area in machine learning [3].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Evolutionary Programming for Developing Recommender Systems based on Collaborative Filtering

Cardenas

Calisaya²,

Martinez-Salinas

et al. 2020

IJACSA

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In the era of internet, several online platforms offer many items to users. Users could spend a lot of time to find (or not) some items they are interested, sometimes, they will probably not find the desired items. An effective strategy to overcome this problem is a recommender system, one of the most popular applications of machine learning. Recommender systems select most appropriate items to an specific user based on previous information between items and users, and they are developed using diffeent approaches. One of the most successful approach for developing recommender systems is collaborative filtering, which can filter out items that a user might like based on reactions of users with similar profiles. Often, traditional recommender systems only consider precision as evaluation metric of performance, however, others metrics (like recall, diversity, novelty, etc) are also important. Unfortunately, some metrics are conflicting, e.g., precision impacts negatively on other metrics. This paper presents a multi-objective evolutionary programming method for developing a recommender system, which is based on a new collaborative filtering technique, while maximizes the recall for a given precision, The new collaborative filtering technique uses three components for recommending an item to a user: 1) clustering of users; 2) a previous memory-based prediction; and 3) five decimal parameters (threshold average clustering, threshold penalty, threshold incentive, weight attached to average clustering and weight attached to Pearson correlation). The multiobjective evolutionary programming optimizes the clustering of users and the five decimal parameters, while, it searches maximizes both similarity precision and recall objectives. A comparison between the proposed method and a previous nonevolutionary method shows that the proposed method improves precision and recall metric on a benchmark database.

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Recommender Systems Challenges and Solutions Survey

Cited by 102 publications

References 19 publications

Temporal-based Optimization to Solve Data Sparsity in Collaborative Filtering

Temporal-based Optimization to Solve Data Sparsity in Collaborative Filtering

The University Recommendation System for Higher Education

Multi-Objective Evolutionary Programming for Developing Recommender Systems based on Collaborative Filtering

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