Improving the pearson similarity equation for recommender systems by age parameter

Aygün, Sercan; Okyay, Savas

doi:10.1109/aieee.2015.7367282

Cited by 9 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…59, No.2B, pp: 934-945 936 b-Aygün S., Okyay, S., [4], in this paper, a new mathematical equation enhancing Pearson similarity measure called age parametrized Pearson similarity equation is proposed and used during the similarity calculation between users. The proposed similarity measure utilizes time information taken from users' ages used in the recommender systems.…”

Section: Al-bakri and Hashimmentioning

confidence: 99%

“…In Table-4, the CPCC measure formula is applied; it is shown from the results that this measure distinguishes between positive and negative impacts of users according to their rating. NAN (divide by zero) values showed up during the calculation (as shown in Table- 4), this is because the rating values for some users has the same median's value of the rating scale. This measure has good impact during neighborhood generation, because only positive similarity values are taken and negative values will be discarded (inverse relation between users).…”

Section: Al-bakri and Hashimmentioning

confidence: 99%

See 1 more Smart Citation

A Modified Similarity Measure for Improving Accuracy of User-Based Collaborative Filtering

AL-Bakri

Hashim

2018

ijs

View full text Add to dashboard Cite

Production sites suffer from idle in marketing of their products because of the lack in the efficient systems that analyze and track the evaluation of customers to products; therefore some products remain untargeted despite their good quality. This research aims to build a modest model intended to take two aspects into considerations. The first aspect is diagnosing dependable users on the site depending on the number of products evaluated and the user's positive impact on rating. The second aspect is diagnosing products with low weights (unknown) to be generated and recommended to users depending on logarithm equation and the number of corated users. Collaborative filtering is one of the most knowledge discovery techniques used positively in recommendation system. Similarity measures are the core operations in collaborative filtering; however, there is a certain deviance through using traditional similarity measures, which decreases the recommendation accuracy. Thus, the proposed model consists of a combination of measures: constraint Pearson correlation, jaccard distance measure and inverse user frequency (IUF).The experimental results implemented on movielens data set using MATLAB show a comparison between the results of the proposed model and some of the traditional similarity measures. The outcome results of the comparison show that the proposed model can be used as a parameter in the prediction process to achieve accurate prediction results during recommendation process.

show abstract

Section: Al-bakri and Hashimmentioning

confidence: 99%

Section: Al-bakri and Hashimmentioning

confidence: 99%

A Modified Similarity Measure for Improving Accuracy of User-Based Collaborative Filtering

AL-Bakri

Hashim

2018

ijs

View full text Add to dashboard Cite

show abstract

“…In the field of recommendation systems, the similarity between users significantly influences the calculation of recommended items [3], enhancing the performance of the recommendation [4]. A common approach involves the calculation of similarity measurements between pairs of users or items [5].…”

Section: Introductionmentioning

confidence: 99%

Employing Data and Process Mining Techniques for Redundancy Detection and Analystics in Business Processes

Trabelsi,

Khtira,

El Asri

2023

ISI

View full text Add to dashboard Cite

The detection, quantification, and scrutiny of redundancies within business processes is pivotal in achieving cost reduction, enhancing efficiency, and ensuring compliance. Redundancies, often leading to inefficiencies, result in escalated costs and errors, thereby detrimentally influencing an organization's overall performance. To counter these issues, data mining and process mining techniques offer promising solutions by identifying and analyzing process redundancies. Data mining, an approach devoted to the analysis of large datasets in order to discern patterns, relationships, and anomalies, has been applied to business processes. It provides insights into redundancies by scrutinizing process-related data, such as event logs, thereby revealing patterns in task executions that may indicate redundancies. In contrast, process mining employs event logs to generate a process model mirroring the actual execution of a process. This actual process model is subsequently contrasted against an expected process model, facilitating the identification of redundancies such as unnecessary activities or loops. Cluster analysis, a technique employed in both data mining and process mining, is exemplified for its capacity to group similar process instances or models based on specified attributes or characteristics. The application of cluster analysis aids in the identification of redundant process models or similar process patterns, thereby enabling further comparison and optimization.

show abstract

“…Concisely, the statistical correlation measurement between vectors is the first step. The vector is either users of the intended system or the items depending on the userbased or item-based similarities (Aygun and Okyay, 2015). The next step is the utilization of the obtained correlation value to attain the numerical prediction.…”

Section: Introductionmentioning

confidence: 99%

İşbirlikçi Filtreleme için Pearson Korelasyonu Üzerine Statik ve Dinamik Önem Ağırlıklandırma Çarpanları Çalışması

Okyay

Aygün

2020

European Journal of Science and Technology

View full text Add to dashboard Cite

Recommender systems as a field of data mining and knowledge discovery have a tremendous impact on movie recommendation platforms. Proper recommendation for the audience, considering profiles, is a measurable argument. By inferencing the linear combinations between some numerical data such as user rating actions, statistical analyses can be done. Thus, any item such as a movie can be recommended or not. The numerical calculation of correlations, namely the similarity weight, should be recomputed before prediction to increase the effect of user similarities for further constant multiplications. This method is named as the significance weighting that processes one more step to stress the impact of similarities. The affinity between users can simply be the total number of co-rated items, or any further inference using more complex computations. In this work, the significance weighting method related to Pearson Correlation is inspected using comparative approaches. The MovieLens dataset, both including ML100K and ML1M releases, are used in the experiments. k-fold cross-validation method is applied in a shifting fashion to increase the number of tests. After having Pearson Correlation Coefficients for user-user similarities, weights are signified using three different approaches. Then, neighbors are sorted to choose the top-N closest users for the user in the test. Concerning experimental results, over two other techniques, an explicit method that utilizes only the co-rated item count is preferred taking its simplicity and performance into account. In the plots of experimental results section, accuracy and error metrics are presented for three different significance weighting approaches. Especially for the ML100K dataset, the simple weighting method outperforms in terms of the error metrics.

show abstract

Improving the pearson similarity equation for recommender systems by age parameter

Cited by 9 publications

References 9 publications

A Modified Similarity Measure for Improving Accuracy of User-Based Collaborative Filtering

A Modified Similarity Measure for Improving Accuracy of User-Based Collaborative Filtering

Employing Data and Process Mining Techniques for Redundancy Detection and Analystics in Business Processes

İşbirlikçi Filtreleme için Pearson Korelasyonu Üzerine Statik ve Dinamik Önem Ağırlıklandırma Çarpanları Çalışması

Contact Info

Product

Resources

About