Interestingnesslab: A Framework for Developing and Using Objective Interestingness Measures

Phan, Lan Phuong; Phan, Nghia Quoc; Nguyen, Ky Minh; Huynh, Hung Huu; Huynh, Hiep Xuan; Guillet, Fabrice

doi:10.1007/978-3-319-49073-1_33

Cited by 10 publications

(7 citation statements)

References 6 publications

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“…This measure is used by the sub-recommendation model based on k nearest neighbors. KnnIR is built on KIR (Kernel Implicative Rating) as in (11…”

Section: A Impilcative Rating Measuresmentioning

confidence: 99%

“…Calculating and returning the rating value for each item  based on the measure KnnIR defined in (11). These experimental datasets need to be preprocessed to increase the accuracy of recommendations.…”

Section: E User Based Recommendationmentioning

confidence: 99%

“…The proposed hybrid recommendation model is developed in the R language and uses the functions that we built in the Interestingnesslab tool [11]. Besides, we also use some recommendation models of the recommenderlab package 2 to compare with the proposed model.…”

Section: B Experimental Toolmentioning

confidence: 99%

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Implicative Rating-Based Hybrid Recommendation Systems

Phan¹,

Huynh²,

Huynh³

2018

IJMLC

Self Cite

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This paper proposes the implicative rating measures built on the statistical implicative measures. The paper also proposes the hybrid recommendation model-the combination of the user-based collaborative filtering approach and the association rule based approach using the implicative rating measures-to suggest a list of top N items to active users. The proposed hybrid model are compared to some existing models on two datasets MSWeb and CourseRegistraion. The experimental results show that the performance of the proposed model is higher than that of the compared models.

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“…This measure is used by the sub-recommendation model based on k nearest neighbors. KnnIR is built on KIR (Kernel Implicative Rating) as in (11…”

Section: A Impilcative Rating Measuresmentioning

confidence: 99%

Section: E User Based Recommendationmentioning

confidence: 99%

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Implicative Rating-Based Hybrid Recommendation Systems

Phan¹,

Huynh²,

Huynh³

2018

IJMLC

Self Cite

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show abstract

“…We developed the recommendation model using rule based implicative measure in the R language, and integrated it in the Interestingnesslab tool [12]. Besides, the three existing models of recommenderlab package [13] are also used for the comparison purpose.…”

Section: ) Experimental Toolmentioning

confidence: 99%

Recommendation using Rule based Implicative Rating Measure

Phan¹,

Huynh²,

Huynh³

2018

ijacsa

Self Cite

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The paper presents a rule based implicative rating measure to calculate the ratings of users on items. The paper also presents a new model using the ruleset with the rule length of 2 and the proposed measure to suggest to users the list of items with the highest ratings. The new model is compared to the three existing models that use items (such as the popular items, the items with highest similarities, and the items with strong relationships) to make the suggestion. The experiments on the MSWeb dataset and the MovieLens dataset indicate that the proposed recommendation model has the higher performace (via the Precision-Recall and the ROC curves) than the compared models for most of the given.

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“…The proposed recommendation model is developed in the R language and uses the functions that we built in the Interestingnesslab tool [12]. Besides, we also use some recommendation models of the recommenderlab package 1 , to compare with the proposed model.…”

Section: ) Experimental Toolmentioning

confidence: 99%

User based Recommender Systems using Implicative Rating Measure

Phan¹,

Huynh²,

Huynh³

2017

ijacsa

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Abstract-This paper proposes the implicative rating measure developed on the typicality measure. The paper also proposes a new recommendation model presenting the top N items to the active users. The proposed model is based on the user-based collaborative filtering approach using the implicative intensity measure to find the nearest neighbors of the active users, and the proposed measure to predict users' ratings for items. The model is evaluated on two datasets MovieLens and CourseRegistration, and compared to some existing models such as: the item based collaborative filtering model using the Jaccard measure, the user based collaborative filtering model using Jaccard measure, the popular items based model, the latent factor based model, and the association rule based model using the confidence measure. The experimental results show that the performance of the proposed model is better when compared to other five models.

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Interestingnesslab: A Framework for Developing and Using Objective Interestingness Measures

Cited by 10 publications

References 6 publications

Implicative Rating-Based Hybrid Recommendation Systems

Implicative Rating-Based Hybrid Recommendation Systems

Recommendation using Rule based Implicative Rating Measure

User based Recommender Systems using Implicative Rating Measure

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