Recommender System Based on Temporal Models: A Systematic Review

Abstract

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.

Latent based temporal optimization approach for improving the performance of collaborative filtering

“…Summarily, the existing temporal-based approaches have addressed several limitations of recommender systems such as sparsity (Zhang et al, 2020a;Idrissi & Zellou, 2020;Chu et al, 2020), drift issue (Rabiu et al, 2020;Al-Hadi et al, 2017a) and time decay issue (Koren, 2009;Ye & Eskenazi, 2014;Al-Hadi et al, 2018b). Each reviewed approach in this article has one or two research gaps, e.g., learning the personalized features, the drift preferences, and the popularity decay.…”

Re-Enrichment Learning: Metadata Saliency for the Evolutive Personalization of a Recommender System

“…α ≥ 1). In the second term, the inversed substitution between two nodes' attributes is adopted to produce the similarity as a weight value (see Equations (7) and (8)). The attributes of nodes have been normalized to remove scale dependency.…”

“…However, recent academic studies and industrial providers have both emphasized that one of the main measurement targets is improved user retention [5,6]. In spite of the gains in theoretical accuracy, it was unclear that the winning strategy in accuracy would result always in increasing business value [7].…”

Peer Review #1 of "Latent based temporal optimization approach for improving the performance of collaborative filtering (v0.1)"

“…Summarily, the existing temporal-based approaches have addressed several limitations of recommender systems such as sparsity (Zhang et al, 2020a;Idrissi and Zellou, 2020;Chu et al, 2020), drift issue (Rabiu et al, 2020;Al-Hadi et al, 2017a) and time decay issue (Koren, 2009;Ye and Eskenazi, 2014;Al-Hadi et al, 2018b). Each reviewed approach in this article has one or two research gaps, e.g., learning the personalized features, the drift preferences, and the popularity decay.…”