Recommender system implementations for embedded collaborative filtering applications

Pajuelo-Holguera, Francisco; Gómez–Pulido, Juan A.; Ortega, Fernando; Granado-Criado, José M.

doi:10.1016/j.micpro.2020.102997

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…The root mean square error (RMSE) function is employed for performance evaluation. RMSE has been utilized in many prediction approaches [15], [28] to evaluate the performance of the CF technique. A lower RMSE value indicates a higher prediction accuracy.…”

Section: B Evaluation and Benchmark Methodsmentioning

confidence: 99%

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%

Temporal-based Optimization to Solve Data Sparsity in Collaborative Filtering

Al-Hadi¹,

Alomari²,

Alshari³

et al. 2020

IJACSA

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“…The main idea is to decompose the original user-commodity rating matrix into low-rank potential matrix through technologies like singular value decomposition and then obtain the prediction result through an analysis [15]. Matrix decomposition method has relieved the sparsity problem of CF algorithm to a certain degree, but cold start problem remains to be solved, so it is impossible to predict the probability for user to purchase new commodities with difficult similarity calculation [16,17]. Furthermore, CF algorithms transform the prediction problem of user purchasing behaviors into processing of rating prediction problem, and the prediction result highly depends on user rating information for commodities.…”

Section: Collaborative Filtering Algorithmmentioning

confidence: 99%

Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms

Zhang

Dong

2020

J Wireless Com Network

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With the rapid development of wireless communication network, M-Commerce has achieved great success. Users leave a lot of historical behavior data when shopping on the M-Commerce platform. Using these data to predict future purchasing behaviors of the users will be of great significance for improving user experience and realizing mutual benefit and win-win result between merchant and user. Therefore, a sample balance-based multi-perspective feature ensemble learning was proposed in this study as the solution to predicting user purchasing behaviors, so as to acquire user’s historical purchasing behavioral data with sample balance. Influence feature of user purchasing behaviors was extracted from three perspectives—user, commodity and interaction, in order to further enrich the feature dimensions. Meanwhile, feature selection was carried out using XGBSFS algorithm. Large-scale real datasets were experimented on Alibaba M-Commerce platform. The experimental results show that the proposed method has achieved better prediction effect in various evaluation indexes such as precision and recall rate.

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“…The main parallelization strategy for PMF is described in [49]. As we can see in Algorithm 1, two consecutive loops can be parallelized after initialization in order to update the corresponding factorized matrices for each user/item.…”

Section: Pmf Designmentioning

confidence: 99%

Performance of Two Approaches of Embedded Recommender Systems

2020

Self Cite

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Nowadays, highly portable and low-energy computing environments require programming applications able to satisfy computing time and energy constraints. Furthermore, collaborative filtering based recommender systems are intelligent systems that use large databases and perform extensive matrix arithmetic calculations. In this research, we present an optimized algorithm and a parallel hardware implementation as good approach for running embedded collaborative filtering applications. To this end, we have considered high-level synthesis programming for reconfigurable hardware technology. The design was tested under environments where usual parameters and real-world datasets were applied, and compared to usual microprocessors running similar implementations. The performance results obtained by the different implementations were analyzed in computing time and energy consumption terms. The main conclusion is that the optimized algorithm is competitive in embedded applications when considering large datasets and parallel implementations based on reconfigurable hardware.

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Recommender system implementations for embedded collaborative filtering applications

Cited by 11 publications

References 22 publications

Temporal-based Optimization to Solve Data Sparsity in Collaborative Filtering

Temporal-based Optimization to Solve Data Sparsity in Collaborative Filtering

Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms

Performance of Two Approaches of Embedded Recommender Systems

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