Effect of Dataset Size on Efficiency of Collaborative Filtering Recommender Systems with Multi-clustering as a Neighbourhood Identification Strategy

Kużelewska, Urszula

doi:10.1007/978-3-030-50420-5_25

Cited by 14 publications

(15 citation statements)

References 22 publications

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“…Besides that, we also plan to add more attributes, such as time, in order to generate high-quality recommendations. Finally, we want to investigate theoretically and empirically the effect of dataset size on efficiency of collaborative filtering recommender systems [28].…”

Section: Discussionmentioning

confidence: 99%

Experimental Analysis of Friend-And-Native Based Location Awareness for Accurate Collaborative Filtering

Kang

2021

Applied Sciences

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Location-based recommender systems have gained a lot of attention in both commercial domains and research communities where there are various approaches that have shown great potential for further studies. However, there has been little attention in previous research on location-based recommender systems for generating recommendations considering the locations of target users. Such recommender systems sometimes recommend places that are far from the target user’s current location. In this paper, we explore the issues of generating location recommendations for users who are traveling overseas by taking into account the user’s social influence and also the native or local expert’s knowledge. Accordingly, we have proposed a collaborative filtering recommendation framework called the Friend-And-Native-Aware Approach for Collaborative Filtering (FANA-CF), to generate reasonable location recommendations for users. We have validated our approach by systematic and extensive experiments using real-world datasets collected from Foursquare TM. By comparing algorithms such as the collaborative filtering approach (item-based collaborative filtering and user-based collaborative filtering) and the personalized mean approach, we have shown that our proposed approach has slightly outperformed the conventional collaborative filtering approach and personalized mean approach.

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Section: Discussionmentioning

confidence: 99%

Experimental Analysis of Friend-And-Native Based Location Awareness for Accurate Collaborative Filtering

Kang

2021

Applied Sciences

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“…Generally, scalability is a critical issue that should be very carefully considered, especially in large scale applications [13]. For example, a method achieving very high effectiveness, but it requires a lot of training time exceeding the time that the recommender prediction model should be periodically updated (e.g.…”

Section: Discussionmentioning

confidence: 99%

A Graph-based Method for Session-based Recommendations

Delianidi

Salampasis

Diamantaras

et al. 2020

24th Pan-Hellenic Conference on Informatics

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We present a graph-based approach for the data management tasks and the efficient operation of a system for sessionbased next-item recommendations. The proposed method can collect data continuously and incrementally from an ecommerce web site, thus seemingly prepare the necessary data infrastructure for the recommendation algorithm to operate without any excessive training phase. Our work aims at developing a recommender method that represents a balance between data processing and management efficiency requirements and the effectiveness of the recommendations produced. We use the Neo4j graph database to implement a prototype of such a system. Furthermore, we use an industry dataset corresponding to a typical e-commerce session-based scenario, and we report on experiments using our graph-based approach and other state-of-the-art machine learning and deep learning methods.

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“…Advancement of web-based e-commerce and other applications has resulted in the enormous growth in the size of the users and items [10] . Empirical studies have not been able to consistently ascertain whether the memory-based approach surpasses the model-based approach in terms of computational efficiency or vice versa [11][12][13][14][15][16] . In general, the model-based CF performs better than memory-based CF in terms of accuracy when the rating matrix is highly sparse [11,16] .…”

Section: Introductionmentioning

confidence: 99%

Optimal Dependence of Performance and Efficiency of Collaborative Filtering on Random Stratified Subsampling

Poudel

Bikdash

2022

Big Data Min. Anal.

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Dropping fractions of users or items judiciously can reduce the computational cost of Collaborative Filtering (CF) algorithms. The effect of this subsampling on the computing time and accuracy of CF is not fully understood, and clear guidelines for selecting optimal or even appropriate subsampling levels are not available. In this paper, we present a Density-based Random Stratified Subsampling using Clustering (DRSC) algorithm in which the desired Fraction of Users Dropped (FUD) and Fraction of Items Dropped (FID) are specified, and the overall density during subsampling is maintained. Subsequently, we develop simple models of the Training Time Improvement (TTI) and the Accuracy Loss (AL) as functions of FUD and FID, based on extensive simulations of seven standard CF algorithms as applied to various primary matrices from MovieLens, Yahoo Music Rating, and Amazon Automotive data. Simulations show that both TTI and a scaled AL are bi-linear in FID and FUD for all seven methods. The TTI linear regression of a CF method appears to be same for all datasets. Extensive simulations illustrate that TTI can be estimated reliably with FUD and FID only, but AL requires considering additional dataset characteristics. The derived models are then used to optimize the levels of subsampling addressing the tradeoff between TTI and AL. A simple sub-optimal approximation was found, in which the optimal AL is proportional to the optimal Training Time Reduction Factor (TTRF) for higher values of TTRF, and the optimal subsampling levels, like optimal FID/(1-FID), are proportional to the square root of TTRF.

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Effect of Dataset Size on Efficiency of Collaborative Filtering Recommender Systems with Multi-clustering as a Neighbourhood Identification Strategy

Cited by 14 publications

References 22 publications

Experimental Analysis of Friend-And-Native Based Location Awareness for Accurate Collaborative Filtering

Experimental Analysis of Friend-And-Native Based Location Awareness for Accurate Collaborative Filtering

A Graph-based Method for Session-based Recommendations

Optimal Dependence of Performance and Efficiency of Collaborative Filtering on Random Stratified Subsampling

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