Survey of similarity functions on neighborhood-based collaborative filtering

H Khojamli, J Razmara - Expert Systems with Applications, 2021 - Elsevier
Today, recommender systems play a vital role in the acceleration of searches by internet
users to find what they are interested in. Among the strategies proposed for recommender …

Recommender systems: an overview, research trends, and future directions

PK Singh, PKD Pramanik, AK Dey… - … Journal of Business …, 2021 - inderscienceonline.com
Recommender system (RS) has emerged as a major research interest that aims to help
users to find items online by providing suggestions that closely match their interest. This …

Optimized recommendations by user profiling using apriori algorithm

PK Singh, E Othman, R Ahmed, A Mahmood… - Applied Soft …, 2021 - Elsevier
Collaborative filtering has been the most straightforward and most preferable approach in
the recommender systems. This technique recommends an item to a target user from the …

[HTML][HTML] Boosting the item-based collaborative filtering model with novel similarity measures

HI Abdalla, AA Amer, YA Amer, L Nguyen… - International Journal of …, 2023 - Springer
Collaborative filtering (CF), one of the most widely employed methodologies for
recommender systems, has drawn undeniable attention due to its effectiveness and …

An improved item-based collaborative filtering using a modified Bhattacharyya coefficient and user–user similarity as weight

PK Singh, S Sinha, P Choudhury - Knowledge and Information Systems, 2022 - Springer
Item-based filtering technique is a collaborative filtering algorithm for recommendations.
Correlation-based similarity measures such as cosine similarity, Pearson correlation, and its …

Aggregated Relative Similarity (ARS): a novel similarity measure for improved personalised learning recommendation using hybrid filtering approach

S Pal, PK Dutta Pramanik, P Choudhury - Multimedia Tools and …, 2024 - Springer
To improve the effectiveness of online learning, the learning materials recommendation is
required to be personalised to the learner material recommendations must be personalized …

Collaborative filtering in recommender systems: Technicalities, challenges, applications, and research trends

PK Singh, PKD Pramanik, P Choudhury - New Age Analytics, 2020 - taylorfrancis.com
The rapid development and extensive use of recommender systems (RSs) have changed
the face of online service experience. The enormous data generated and the complexity …

An overlapping clustering approach for precision, diversity and novelty-aware recommendations

CE Berbague, NE Karabadji, H Seridi… - Expert Systems with …, 2021 - Elsevier
Recommender systems aim to provide users with recommendations of quality. New
evaluation metrics such as diversity, have taken an increasing interest in a wide spectrum of …

An improved similarity calculation method for collaborative filtering-based recommendation, considering neighbor's liking and disliking of categorical attributes of items

PK Singh, PKD Pramanik… - Journal of information and …, 2019 - Taylor & Francis
Similarity measures play an important role in the accuracy of collaborative filtering based
recommendation. Due to non-availability of adequate co-rated users, the accuracy of …

[HTML][HTML] Utilizing alike neighbor influenced similarity metric for efficient prediction in collaborative filter-approach-based recommendation system

RK Singh, PK Singh, JP Singh, AK Singh… - Applied Sciences, 2022 - mdpi.com
The most popular method collaborative filter approach is primarily used to handle the
information overloading problem in E-Commerce. Traditionally, collaborative filtering uses …