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

Singh, Pradeep Kumar; Sinha, Shreyashee; Choudhury, Prasenjit

doi:10.1007/s10115-021-01651-8

Cited by 24 publications

(9 citation statements)

References 43 publications

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“…* Item-based collaborative filtering utilises the similarity values of items for predicting the target item [91]. The basic assumption of item-based CF is that user prefers similar items that he or she liked in the past [92].…”

Section: Ai Techniques For the Ux Of Recommender Systemsmentioning

confidence: 99%

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Virvou¹

2023

IDT

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Among the primary aims of Artificial Intelligence (AI) is the enhancement of User Experience (UX) by providing deep understanding, profound empathy, tailored assistance, useful recommendations, and natural communication with human interactants while they are achieving their goals through computer use. To this end, AI is used in varying techniques to automate sophisticated functions in UX and thereby changing what UX is apprehended by the users. This is achieved through the development of intelligent interactive systems such as virtual assistants, recommender systems, and intelligent tutoring systems. The changes are well received, as technological achievements but create new challenges of trust, explainability and usability to humans, which in turn need to be amended by further advancements of AI in reciprocity. AI can be utilised to enhance the UX of a system while the quality of the UX can influence the effectiveness of AI. The state of the art in AI for UX is constantly evolving, with a growing focus on designing transparent, explainable, and fair AI systems that prioritise user control and autonomy, protect user data privacy and security, and promote diversity and inclusivity in the design process. Staying up to date with the latest advancements and best practices in this field is crucial. This paper conducts a critical analysis of published academic works and research studies related to AI and UX, exploring their interrelationship and the cause-effect cycle between the two. Ultimately, best practices for achieving a successful interrelationship of AI in UX are identified and listed based on established methods or techniques that have been proven to be effective in previous research reviewed.

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Section: Ai Techniques For the Ux Of Recommender Systemsmentioning

confidence: 99%

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Virvou¹

2023

IDT

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“…Sinha et al introduced a novel similarity measure, a modified Bhattacharya coefficient to compute user -user similarity in sparse environments and to use it as a weight in the IBCF. The authors showed an improvement in the performance of RS by experimenting the similarity measure on the MovieLens dataset (8) . Fethi Fkih compared thirteen different similarity measures commonly used in CFRS and observed that UBCF and IBCF do not rely on the same similarity measure to provide the best system performance.…”

Section: Related Workmentioning

confidence: 99%

Implementation of Nearest Co-cluster Collaborative Filtering using a Novel Similarity Index

Kataria¹,

Batra²

2023

IJST

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Objectives: To implement Nearest Co-cluster Collaborative Filtering (NCCF) using a novel similarity index and evaluate its performance in sparse environments. Methods: An experimental and comparative evaluation of the proposed technique is performed using Python's built -in data analysis packages. As a preliminary step, preprocessing is performed on the explicit book -crossing rating dataset acquired from the GroupLens research lab database. After preprocessing, the nearest co-cluster algorithm based on a novel similarity index is applied to achieve partial matching of users' preferences. The proposed nearest co-cluster algorithm is experimented against two distinct types of co-clustering algorithms: Bimax for constant coclustering (abbreviated as NCCF -B) and xMotif for coherent co-clustering (abbreviated as NCCF -X). In addition, a co-clustering package CoClust and the toolkit BIDEAL are utilized for co-clusters visualization. Findings: Extensive performance evaluation findings of the proposed technique are provided, and the technique has been found to be promising for generating pertinent recommendations despite data sparsity. The technique can significantly reveal the dualism between users and books through co-clustering and partial matching of users' preferences. Compared in terms of precision, recall, and F1 score, NCCF -B and NCCF -X outperformed Item -Based CF (IBCF), User -Based CF (UBCF) and Clustering -Based CF (CBCF). NCCF -B and NCCF -X outperformed with a precision difference of over 30% and a recall difference of 10%. When comparing two co-clustering algorithms, NCCF -X outperformed NCCF -B slightly but consistently. The differences in precision and recall are approximately 3% and 2%, respectively. However, the CBCF technique outperformed the other considered techniques in terms of execution time. Novelty : NCCF is robust in sparse environments due to its ability in revealing the dualism between users and books and in capturing the partial matching of users' preferences.

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“…It is mainly categorized into memory-based and model-based techniques. Out of these two, the memory-based approach is most popular and is subdivided into user-based [18] and item-based [19] approaches. Many similarity measures, i.e., Modified Proximity Impact Popularity (MPIP) [20], RJaccard [21], TMJ [22], etc., have been introduced in recent years.…”

Section: Literature Reviewmentioning

confidence: 99%

TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System

et al. 2022

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In recent years, commercial platforms have embraced recommendation algorithms to provide customers with personalized recommendations. Collaborative Filtering is the most widely used technique of recommendation systems, whose accuracy is primarily reliant on the computed similarity by a similarity measure. Data sparsity is one problem that affects the performance of the similarity measures. In addition, most recommendation algorithms do not remove noisy data from datasets while recommending the items, reducing the accuracy of the recommendation. Furthermore, existing recommendation algorithms only consider historical ratings when recommending the items to users, but users’ tastes may change over time. To address these issues, this research presents a Deep Neural Network based on Time Decay (TD-DNN). In the data preprocessing phase of the model, noisy ratings are detected from the dataset and corrected using the Matrix Factorization approach. A power decay function is applied to the preprocessed input to provide more weightage to the recent ratings. This non-noisy weighted matrix is fed into the Deep Learning model, consisting of an input layer, a Multi-Layer Perceptron, and an output layer to generate predicted ratings. The model’s performance is tested on three benchmark datasets, and experimental results confirm that TD-DNN outperforms other existing approaches.

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

Cited by 24 publications

References 43 publications

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Implementation of Nearest Co-cluster Collaborative Filtering using a Novel Similarity Index

TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System

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