Improving Collaborative Filtering's Rating Prediction Quality by Considering Shifts in Rating Practices

Margaris, Dionisis; Vassilakis, Costas

doi:10.1109/cbi.2017.24

Cited by 16 publications

(39 citation statements)

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“…Improvement in accuracy indicates that the DA clusters algorithm is able to follow more accurately shifts in rating prediction practices. The DA clusters algorithm also introduces significant space savings in comparison to the DA previous and DA vicinity algorithms [7].…”

Section: Introductionmentioning

confidence: 99%

“…After formulating rating time clusters, DA clusters computes one dynamic average per cluster; this dynamic average is then used in the rating prediction process. To validate our approach, we present an extensive evaluation, comparing the presented algorithm against the DA previous and DA vicinity algorithms proposed in [7] and the plain CF algorithm, which is used as a yardstick. Experiments have shown that using the per cluster dynamic averages, which are computed by the DA clusters algorithm leads to more accurate predictions as compared to the per-rating dynamic averages that are computed by the DA previous and DA vicinity algorithms proposed in [7].…”

Section: Introductionmentioning

confidence: 99%

“…To validate our approach, we present an extensive evaluation, comparing the presented algorithm against the DA previous and DA vicinity algorithms proposed in [7] and the plain CF algorithm, which is used as a yardstick. Experiments have shown that using the per cluster dynamic averages, which are computed by the DA clusters algorithm leads to more accurate predictions as compared to the per-rating dynamic averages that are computed by the DA previous and DA vicinity algorithms proposed in [7]. Improvement in accuracy indicates that the DA clusters algorithm is able to follow more accurately shifts in rating prediction practices.…”

Section: Introductionmentioning

confidence: 99%

“…Margaris and Vassilakis [7] introduce the concept of dynamic user rating averages which follow the users' marking practices shifts and present two alternative algorithms, namely the DA vicinity and DA previous algorithms, for computing a user's dynamic averages. These algorithms are validated in the context of user-user CF, and have been found to achieve better rating prediction accuracy than the plain CF algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed algorithm, as well as the two algorithms presented in [7], are based on the exploitation of timestamp information which is associated with ratings; hence in this work, we use the Amazon datasets [8], [9], the MovieLens datasets [10], [11] and the Netflix dataset [12], which include the ratings' timestamps. 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.…”

Section: Introductionmentioning

confidence: 99%

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Using Time Clusters for Following Users’ Shifts in Rating Practices

Margaris

Vassilakis

2017

CSIMQ

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%