A graph-based recommendation approach for highly interactive platforms. (15th December 2021)

• Record Type: Journal Article
• Authors: Ficel, Hemza; Haddad, Mohamed Ramzi; Baazaoui Zghal, Hajer
• Is Part Of: Expert systems with applications. Volume 185(2021)
• Publication Date: 2021-12-15
• Subjects: Recommender systems -- Stream analysis -- Consumption behavior modeling and prediction -- Ranking recommendation approach -- Knowledge graphs; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115555
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Ranking recommendation approach for data streams recommendation. Heterogeneous information network modeling psychological factors that drive users' decisions. Incremental learning at scale and with low latency. A graph ranking measure to infer online consumers' perceived value. Framework for benchmarking recommender systems under streaming settings. Abstract: Highly interactive and dynamic marketplaces shake the underlying hypothesis of established recommendation approaches assuming static offerings and sparse interaction data. Such markets show different dynamics due to goods/contents volatility and public accessibility. This imposes new challenges to recommender systems since they are required to handle unbounded data streams with high velocity, volume and variability while operating at scale and in real-time. Moreover, due to the high rates of new offerings introduction and obsolescence, low latency modeling and inference are needed to keep an up-to-date understanding of the market. In this paper, we propose a recommendation approach addressing the specific challenges of real-time stream recommendation in highly interactive marketplaces. The approach is based on several psychological theories to model consumers' perceived value towards items. Besides, a ranking measure is defined on a heterogeneous information network to infer the factors that drive consumers' decisions. With data relationships at its center, this data model is strongly efficient while ensuring free and flexible knowledge evolution as data evolves. It allows low latency incremental learning at scale while providing dynamic recommendations. Several comparative experiments were conducted to validate the potential of this approach in different use cases requiring offline, online, static or dynamic recommendations.