A Hybrid CBR Approach for the Long Tail Problem in Recommender Systems

Alshammari, Gharbi; Jorro-Aragoneses, Jose L.; Kapetanakis, Stelios; Petridis, Miltos; Recio-Garcí­a, Juan A.; Dí­az-Agudo, Belén

doi:10.1007/978-3-319-61030-6_3

Cited by 17 publications

(12 citation statements)

References 19 publications

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“…It should also be noted that lower values are better. MAE has been widely used in previous research for predicting the accuracy of recommender systems [3,19,20].…”

Section: Recommendation Methodsmentioning

confidence: 99%

Reproducibility of Experiments in Recommender Systems Evaluation

Polatidis

Kapetanakis

Pimenidis

et al. 2018

IFIP Advances in Information and Communication Technology

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Recommender systems evaluation is usually based on predictive accuracy metrics with better scores meaning recommendations of higher quality. However, the comparison of results is becoming increasingly difficult, since there are different recommendation frameworks and different settings in the design and implementation of the experiments. Furthermore, there might be minor differences on algorithm implementation among the different frameworks. In this paper, we compare well known recommendation algorithms, using the same dataset, metrics and overall settings, the results of which point to result differences across frameworks with the exact same settings. Hence, we propose the use of standards that should be followed as guidelines to ensure the replication of experiments and the reproducibility of the results.

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“…It should also be noted that lower values are better. MAE has been widely used in previous research for predicting the accuracy of recommender systems [3,19,20].…”

Section: Recommendation Methodsmentioning

confidence: 99%

Reproducibility of Experiments in Recommender Systems Evaluation

Polatidis

Kapetanakis

Pimenidis

et al. 2018

IFIP Advances in Information and Communication Technology

Self Cite

View full text Add to dashboard Cite

show abstract

“…In Ref. 22, a switching hybrid approach was proposed to solve the long tail problem in recommendations. A hybrid approach was applied that utilized clustering and genetic algorithms to reduce data sparsity in movie recommendations.…”

Section: Hybrid Recommendation Approachesmentioning

confidence: 99%

Improved Movie Recommendations Based on a Hybrid Feature Combination Method

Alshammari

Kapetanakis

Alshammari

et al. 2019

Vietnam J. Comp. Sci.

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Recommender systems help users find relevant items efficiently based on their interests and historical interactions with other users. They are beneficial to businesses by promoting the sale of products and to user by reducing the search burden. Recommender systems can be developed by employing different approaches, including collaborative filtering (CF), demographic filtering (DF), content-based filtering (CBF) and knowledge-based filtering (KBF). However, large amounts of data can produce recommendations that are limited in accuracy because of diversity and sparsity issues. In this paper, we propose a novel hybrid method that combines user–user CF with the attributes of DF to indicate the nearest users, and compare four classifiers against each other. This method has been developed through an investigation of ways to reduce the errors in rating predictions based on users’ past interactions, which leads to improved prediction accuracy in all four classification algorithms. We applied a feature combination method that improves the prediction accuracy and to test our approach, we ran an offline evaluation using the 1M MovieLens dataset, well-known evaluation metrics and comparisons between methods with the results validating our proposed method.

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“…More recently, a combination of one or more methods called a hybrid recommender system has been applied to overcome the limitations of using one approach and obtain better results [7]. For instance, in [16], a hybrid case based reasoning approach was proposed to solve the long tail problem, which basically refers to items that have few ratings, by switching between collab-orative filtering and content-based filtering. In addition, the authors in [17] implemented a hybrid recommender system that applied clustering technique and an artificial algae algorithm with a multi-level CF approach.…”

Section: Related Workmentioning

confidence: 99%

“…We ran the proposed switching hybrid method and compared with the baseline CF using Euclidean and Pearson similarity, with the CBF and with the method in [16].…”

Section: Comparisonmentioning

confidence: 99%