Enhancing User Rating Database Consistency Through Pruning

Margaris, Dionisis; Vassilakis, Costas

doi:10.1007/978-3-662-55947-5_3

Cited by 11 publications

(7 citation statements)

References 39 publications

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“…However, since the MF-based approaches always produce a rating prediction when these algorithms are applied to sparse datasets, the predictions involving items or users having very few ratings actually degenerate to dataset-dependent constant values, which are not considered as personalized predictions. Furthermore, the rating prediction accuracy of these algorithms is relatively poor in these cases [40,41]. Guan et al [42] address this issue by proposing an upgraded singular value decomposition (SVD) model that incorporates the MF algorithms with rating completion inspired by active learning, as well as a multi-layer ESVD model that further improves rating prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…None of the above mentioned works target density increase of sparse CF datasets, leveraging on the one hand the CF rating prediction coverage and, on the other hand, facilitating the application of techniques targeting at rating prediction accuracy, such as matrix factorization and pruning of old user ratings [39,40,50], which cannot be successfully applied in sparse CF datasets.…”

Section: Related Workmentioning

confidence: 99%

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An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings

et al. 2020

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Collaborative filtering algorithms formulate personalized recommendations for a user, first by analysing already entered ratings to identify other users with similar tastes to the user (termed as near neighbours), and then using the opinions of the near neighbours to predict which items the target user would like. However, in sparse datasets, too few near neighbours can be identified, resulting in low accuracy predictions and even a total inability to formulate personalized predictions. This paper addresses the sparsity problem by presenting an algorithm that uses robust predictions, that is predictions deemed as highly probable to be accurate, as derived ratings. Thus, the density of sparse datasets increases, and improved rating prediction coverage and accuracy are achieved. The proposed algorithm, termed as CFDR, is extensively evaluated using (1) seven widely-used collaborative filtering datasets, (2) the two most widely-used correlation metrics in collaborative filtering research, namely the Pearson correlation coefficient and the cosine similarity, and (3) the two most widely-used error metrics in collaborative filtering, namely the mean absolute error and the root mean square error. The evaluation results show that, by successfully increasing the density of the datasets, the capacity of collaborative filtering systems to formulate personalized and accurate recommendations is considerably improved.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings

et al. 2020

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“…Works such as [18], [19] and [43] also assert that a tradeoff between coverage and accuracy exists, and in order to obtain a single measure for rating the suitability of each algorithm, the harmonic mean (HM) of these measures can be adopted; this is analogous to the goal of maximizing the HM of precision and recall -termed the F1 measure -in information retrieval [44]). Towards this direction, we adopt the following formula introduced in [43]: (6) where aAlg is a rating prediction algorithm and Alg is the set of all algorithms participating in the evaluation.…”

Section: The Amazon "Videogames" Datasetmentioning

confidence: 99%

“…It is worth noting that the proposed algorithm can be combined with other techniques that have been proposed for either improving prediction accuracy in CF-based systems, including consideration of social network data (e.g. [13], [14], [15]), location data [16], [17] and pruning of old user ratings [18], [19], or techniques for speeding up prediction computation time, such as clustering [20], [21], [22]. The rest of the article is structured as follows: Section 2 overviews related work, while Section 3 introduces the proposed algorithm and briefly describes the dynamic average-based algorithms presented in [7] for self-containment purposes.…”

Section: Introductionmentioning

confidence: 99%

Using Time Clusters for Following Users’ Shifts in Rating Practices

Margaris

Vassilakis

2018

J. Complex Syst. Inform. Model. Q.

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Abstract. Users that enter ratings for items follow different rating practices, in the sense that, when rating items, some users are more lenient, while others are stricter. This aspect is taken into account by the most widely used similarity metric in user-user collaborative filtering, namely, the Pearson Correlation, which adjusts each individual user rating by the mean value of the ratings entered by the specific user, when computing similarities. However, a user's rating practices change over time, i.e. a user could start as strict and subsequently become lenient or vice versa. In that sense, the practice of using a single mean value for adjusting users' ratings is inadequate, since it fails to follow such shifts in users' rating practices, leading to decreased rating prediction accuracy. In this work, we address this issue by using the concept of dynamic averages introduced earlier and we extend earlier work by (1) introducing the concept of rating time clusters and (2) presenting a novel algorithm for calculating dynamic user averages and exploiting them in useruser collaborative, filtering implementations. The proposed algorithm incorporates the aforementioned concept and is able to follow more successfully shifts in users' rating practices. It has been evaluated using numerous datasets, and has been found to introduce significant gains in rating prediction accuracy, while outperforming the dynamic average computation approaches that are presented earlier.

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“…Recommender systems (RSs) continuously augment their information repositories with data from diverse sources, ranging from smartphones and wearable devices to websites and social networks (SNs), to promote the formulation of successful personalized recommendations in a wide range of domains, from consumer products, such as books, office supplies and CDs, to travel and leisure as well as from restaurants and movies to smartphone apps [1]. Collaborative filtering (CF) is a widely used approach for making recommendations, stemming from user behavior and actions.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy

et al. 2020

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When information from traditional recommender systems is augmented with information about user relationships that social networks store, more successful recommendations can be produced. However, this information regarding user relationships may not always be available, since some users may not consent to the use of their social network information for recommendations or may not have social network accounts at all. Moreover, the rating data (categories and characteristics of products) may be unavailable for a recommender system. In this paper, we present an algorithm that can be applied in any social network-aware recommender system that utilizes the users' ratings on items and users' social relations. The proposed algorithm addresses the issues of limited social network information or limited collaborative filtering information for some users by adapting its behavior, taking into account the density and utility of each user's social network and collaborative filtering neighborhoods. Through this adaptation, the proposed algorithm achieves considerable improvement in rating prediction accuracy. Furthermore, the proposed algorithm can be easily implemented in recommender systems.

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Enhancing User Rating Database Consistency Through Pruning

Cited by 11 publications

References 39 publications

An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings

An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings

Using Time Clusters for Following Users’ Shifts in Rating Practices

An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy

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