An efficient recommendation generation using relevant Jaccard similarity

Bag, Sujoy; Kumar, Somesh; Tiwari, Manoj Kumar

doi:10.1016/j.ins.2019.01.023

Cited by 231 publications

(91 citation statements)

References 40 publications

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“…User similarity calculation is an important technique for collaborative filtering methods. Traditional techniques, including the Cosine similarity model [47], Jaccard similarity model [48], and HP (Hub Promoted) model [49], are widely used. Another widely used technique is the number of common neighbors between users, which considers the more common neighbor friends between users, the higher the similarity between them [50].…”

Section: Mobile Marketing Recommendation Methodsmentioning

confidence: 99%

Research on Mobile Marketing Recommendation Method Incorporating Layout Aesthetic Preference for Sustainable m-Commerce

2020

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The strong interactivity and size limitation of the mobile interface calls for the utilization of users’ aesthetic preferences to provide better mobile marketing recommendations in order to promote the sustainable development of m-commerce. Existing studies mostly focus on matching user interests by analyzing marketing content properties. The studies for utilizing the layout information and user aesthetic preferences for the layout of the mobile marketing interface from an aesthetic perspective are insufficient. This paper proposes a mobile marketing recommendation method (LAPR) that incorporates layout aesthetic preferences. Based on the traditional content-based and collaborative filtering recommendation methods, this method introduces users’ aesthetic preferences for interface layout into a mobile marketing recommender. From an aesthetic perspective, a new interface layout design quantification method, a user aesthetic preference similarity measurement model, and a recommendation result ranking method are designed. Experiments show that compared to traditional methods, LAPR is significantly higher in recommendation precision in the task for recommending the same content and outperforms traditional methods in recall, precision, and F-metrics in the common recommendation task. We conclude that incorporating aesthetic preference for layout can improve mobile marketing recommendation quality and promote the sustainable development of m-commerce.

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Section: Mobile Marketing Recommendation Methodsmentioning

confidence: 99%

Research on Mobile Marketing Recommendation Method Incorporating Layout Aesthetic Preference for Sustainable m-Commerce

2020

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“…Where jk U is the weight matrix going between j and k neurons, j S defines the unit condition k and j  described the threshold function of neurons j. Several studies [28,[42][43][44][45][46][47][48] defined 0 j  = to verify that the HNN always leads to a decrease in energy monotonically. Each time neuron was connected with jk U ,the value of the connection will be preserved as a stored pattern in an interconnected vector where [36,60] that the constraint of synaptic weight matrix (1) U and does not allow self-loop neuron connection…”

Section: Discrete Hopfield Neural Networkmentioning

confidence: 99%

Modified Election Algorithm in Accelerating the Performance of Hopfield Neural Network for Random kSatisfiability

Abubakar¹,

Sathasivam²,

Mansor³

et al. 2020

Preprint

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Election Algorithm (EA) is a powerful metaheuristics model motivated by phenomena of the socio-political mechanism of the presidential election conducted in many countries. EA is selected as a topic of discussion due to its capability and robustness to carry out complex problems in the random-2SAT logic program. This paper utilizes a hybridized EA assimilated with the Hopfield neural network (HNN) in carrying out random logic program (HNN-R2SATEA). The efficiency of the proposed method was compared with the existing traditional exhaustive search (HNN-R2SATES) model and the recently introduced HNN-R2SATICA model. From the result obtained, clearly proven that based on our proposed hybrid model outperformed other existing model based on the Global Minima Ratio (ZM), Mean Absolute Error (MAE), Bayesian Information Criterion (BIC) and Execution Time (ET). The expected outcome portrays that the EA algorithm outperformed the other two algorithms in doing random-kSAT logic program. The results proved the robustness, effectiveness, and compatibility of the HNN-R2SATEA model.

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“…Authors Bobadilla et al have proposed various similarity measures by exploiting the different contextual information [29,30,31,32].Patra et al [33] have adopted the Bhattacharyya coefficient to define a new similarity measure that also manages the data-sparsity problem. Recently, the Jaccard similarity index [34] has been modified to relevant Jaccard similarity [35] for efficient recommendations.…”

Section:  Collaborativementioning

confidence: 99%

A New Similarity Measure Based on Simple Matching Coefficient for Improving the Accuracy of Collaborative Recommendations

Verma¹,

Aggarwal²

2019

IJITCS

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Recommender Systems (RSs) are essential tools of an e-commerce portal in making intelligent decisions for an individual to obtain product recommendations. Neighborhood-based approaches are traditional techniques for collaborative recommendations and are very popular due to their simplicity and efficiency. Neighborhood-based recommender systems use numerous kinds of similarity measures for finding similar users or items. However, the existing similarity measures function only on common ratings between a pair of users (i.e. ignore the uncommon ratings) thus do not utilize all ratings made by a pair of users. Furthermore, the existing similarity measures may either provide inadequate results in many situations that frequently occur in sparse data or involve very complex calculations. Therefore, there is a compelling need to define a similarity measure that can deal with such issues. This research proposes a new similarity measure for defining the similarities between users or items by using the rating data available in the user-item matrix. Firstly, we describe a way for applying the simple matching coefficient (SMC) to the common ratings between users or items. Secondly, the structural information between the rating vectors is exploited using the Jaccard index. Finally, these two factors are leveraged to define the proposed similarity measure for better recommendation accuracy. For evaluating the effectiveness of the proposed method, several experiments have been performed using standardized benchmark datasets (MovieLens-1M, 10M, and 20M). Results obtained demonstrate that the proposed method provides better predictive accuracy (in terms of MAE and RMSE) along with improved classification accuracy (in terms of precision-recall).

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An efficient recommendation generation using relevant Jaccard similarity

Cited by 231 publications

References 40 publications

Research on Mobile Marketing Recommendation Method Incorporating Layout Aesthetic Preference for Sustainable m-Commerce

Research on Mobile Marketing Recommendation Method Incorporating Layout Aesthetic Preference for Sustainable m-Commerce

Modified Election Algorithm in Accelerating the Performance of Hopfield Neural Network for Random kSatisfiability

A New Similarity Measure Based on Simple Matching Coefficient for Improving the Accuracy of Collaborative Recommendations

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