Modeling Temporal Effectiveness for Context-Aware Web Services Recommendation

Abstract

Context-Aware Recommender System (CARS) aims to not only recommend services similar to those already rated with the highest score, but also provide opportunities for exploring the important role of temporal, spatial and social contexts for personalized web services recommendation. A key step for temporal-based CARS methods is to explore the time decay process of past invocation records to make the Quality of Services (QoS) prediction. However, it is a nontrivial task to model the temporal effects on web services recommendation, due to the dynamic features of contextual information in view of temporal spatial correlations. For instance, in locationaware services recommendation, the user's geographical position would change very frequently as time goes on. In this paper, we propose a Context-Aware Services Recommendation based on Temporal Effectiveness (CASR-TE) method. Inspired by existing time decay approaches, we first present an enhanced temporal decay model combining the time decay function with traditional similarity measurement methods. Then, we model temporal spatial correlations as well as their impacts on the user preference expansion. Finally, we evaluate the CASR-TE method on WS-Dream dataset by evaluation matrices of both RMSE and MAE. Experimental results show that our approach outperforms several benchmark methods with a significant margin.

Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

“…The shifting of service bias is the most used time effect in this category of TARS. Moreover, most continuous time‐aware approaches adopt either collaborative filtering or hybrid filtering …”

“…In this context and because of the problem of unilateral trust relationship and the impracticable hypothesis of trust‐based recommender systems, the concept of social relationship appeared to deal with the cooperation between users …”

Time‐aware service recommendation: Taxonomy, review, and challenges

“…Shifting of service bias is the most used time effect in this category of TARS. Moreover, most continuous time‐aware approaches adopt either collaborative filtering or hybrid filtering …”

“…Well‐known similarity measures such as Pearson Correlation Coefficient and its variants are used in categorical TARS. However, they lack precision because of the difficulty to evaluate the appropriate discrete time span and measure the temporal similarity …”

“…In order to deal with the dynamicity of temporal contexts' features and the relationship between temporal and spatial contexts, Fan et al have proposed a context‐aware services recommendation method based on temporal effectiveness. To predict the QoS of Web services, the decay process of time effectiveness is taken into account.…”