Exploiting Rating Abstention Intervals for Addressing Concept Drift in Social Network Recommender Systems

Margaris, Dionisis; Vassilakis, Costas

doi:10.3390/informatics5020021

Cited by 26 publications

(15 citation statements)

References 35 publications

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“…The DRS has been extensively researched in recent ages across different fields such as multimedia [7,22,62,63] e-commerce [35,[63][64][65][66]85,[97][98][99][100][101], e-documents [67][68][69][102][103][104][105][106][107][108], Travel, Tourism and Places [8,10,13,30,37,109], and others [9,12,87,88,94,[110][111][112][113][114][115][116]. This was said to have started after the Netflix competition in 2009, where the time changing user behaviors were considered to improve recommendation accuracy [70][71][72][73][74][75]…”

Section: Application Domain and The Incorporated Concept Driftsmentioning

confidence: 99%

“…However, the change point of user preference is required to be able to adapt to changes appropriately and timely. 9 [8,14,66,69,83,84,90,131,132] Long-/Short-term Time-dept./ Long-vs-short-term User prefer.…”

Section: Time-dependent Modelmentioning

confidence: 99%

“…Time-Dependent tensor models were also exploited in the literature where time is not explicitly defined in the model [73,74,[129][130][131]. For example, [66] proposed a tensor-based method by using timestamped data with different periodic patterns. Additionally, they explored the bipartite graphs to model the evolution of user preferences over time.…”

Section: Time-dependent Modelmentioning

confidence: 99%

“…Short/Long-term preference modeling: SLPM is another way of approaching sequentially ordered data which focuses on how to separately model the user' short-/and long-term behavior respectively [36,66,133,134]. The SLPM is based on the assumption that each user has potentially two models, one for short-term preferences and another for long-term preferences [26].…”

Section: Time-dependent Modelmentioning

confidence: 99%

“…To this end, [36] stressed the importance of short-term preference model to re-rank the recommendations lists which were proven to have a considerable improvement in terms of accuracy. In a different approach, [66] proposed a model consisting of an offline component and online component to capture the long-term and short-term influences. With the online component, the short-term influence is continuously updated with new incoming data and therefore tends to be more sensitive to the less stable or short-term preferences.…”

Section: Time-dependent Modelmentioning

confidence: 99%

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Recommender System Based on Temporal Models: A Systematic Review

et al. 2020

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Over the years, the recommender systems (RS) have witnessed an increasing growth for its enormous benefits in supporting users’ needs through mapping the available products to users based on their observed interests towards items. In this setting, however, more users, items and rating data are being constantly added to the system, causing several shifts in the underlying relationship between users and items to be recommended, a problem known as concept drift or sometimes called temporal dynamics in RS. Although the traditional techniques of RS have attained significant success in providing recommendations, they are insufficient in providing accurate recommendations due to concept drift problems. These issues have triggered a lot of researches on the development of dynamic recommender systems (DRSs) which is focused on the design of temporal models that will account for concept drifts and ensure more accurate recommendations. However, in spite of the several research efforts on the DRSs, only a few secondary studies were carried out in this field. Therefore, this study aims to provide a systematic literature review (SLR) of the DRSs models that can guide researchers and practitioners to better understand the issues and challenges in the field. To achieve the aim of this study, 87 papers were selected for the review out of 875 total papers retrieved between 2010 and 2019, after carefully applying the inclusion/exclusion and the quality assessment criteria. The results of the study show that concept drift is mostly applied in the multimedia domain, then followed by the e-commerce domain. Also, the results showed that time-dependent neighborhood models are the popularly used temporal models for DRS followed by the Time-dependent Matrix Factorization (TMF) and time-aware factors models, specifically Tensor models, respectively. In terms of evaluation strategy, offline metrics such as precision and recalls are the most commonly used approaches to evaluate the performance of DRS.

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