Peer Review #3 of "Latent based temporal optimization approach for improving the performance of collaborative filtering (v0.2)"

Recommendation systems suggest peculiar products to customers based on their past ratings, preferences, and interests. These systems typically utilize collaborative filtering (CF) to analyze customers’ ratings for products within the rating matrix. CF suffers from the sparsity problem because a large number of rating grades are not accurately determined. Various prediction approaches have been used to solve this problem by learning its latent and temporal factors. A few other challenges such as latent feedback learning, customers’ drifting interests, overfitting, and the popularity decay of products over time have also been addressed. Existing works have typically deployed either short or long temporal representation for addressing the recommendation system issues. Although each effort improves on the accuracy of its respective benchmark, an integrative solution that could address all the problems without trading off its accuracy is needed. Thus, this paper presents a Latent-based Temporal Optimization (LTO) approach to improve the prediction accuracy of CF by learning the past attitudes of users and their interests over time. Experimental results show that the LTO approach efficiently improves the prediction accuracy of CF compared to the benchmark schemes.

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A Particle Swarm Optimization Approach to Fuzzy Case-based Reasoning in the Framework of Collaborative Filtering

International Journal of Rough Sets and Data Analysis ◽

10.4018/ijrsda.2014010104 ◽

2014 ◽

Vol 1 (1) ◽

pp. 48-64 ◽

Cited By ~ 12

Author(s):

Shweta Tyagi ◽

Kamal K. Bharadwaj

Keyword(s):

Particle Swarm Optimization ◽

Collaborative Filtering ◽

Prediction Accuracy ◽

Particle Swarm ◽

Pso Algorithm ◽

Personalized Recommendation ◽

Case Based Reasoning ◽

Optimization Approach ◽

Swarm Optimization ◽

Case Based

The particle Swarm Optimization (PSO) algorithm, as one of the most effective search algorithm inspired from nature, is successfully applied in a variety of fields and is demonstrating fairly immense potential for development. Recently, researchers are investigating the use of PSO algorithm in the realm of personalized recommendation systems for providing tailored suggestions to users. Collaborative filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. However, data sparsity and prediction accuracy are the major concerns related to CF techniques. In order to handle these problems, this paper proposes a novel approach to CF technique by employing fuzzy case-based reasoning (FCBR) augmented with PSO algorithm, called PSO/FCBR/CF technique. In this method, the PSO algorithm is utilized to estimate the features importance and assign their weights accordingly in the process of fuzzy case-based reasoning (FCBR) for the computation of similarity between users and items. In this way, PSO embedded FCBR algorithm is applied for the prediction of missing values in user-item rating matrix and then CF technique is employed to generate recommendations for an active user. The experimental results clearly reveal that the proposed scheme, PSO/FCBR/CF, deals with the problem of sparsity as well as improves the prediction accuracy when compared with other state of the art CF schemes.

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Peer review of a dental curriculum: how one school did it

Journal of Dental Education ◽

10.1002/j.0022-0337.1976.40.11.tb01046.x ◽

1976 ◽

Vol 40 (11) ◽

pp. 761-762

Author(s):

PK Morse ◽

TR Dirksen

Keyword(s):

Peer Review ◽

Dental Curriculum

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