A Personalized Recommendation for Web API Discovery in
Social Web of Things
Marwa Meissa1,
Saber Benharzallah2, Laid Kahloul1, and Okba Kazar1
1LINFI Laboratory, Biskra University, Algeria
2Department of Computer Science,
LAMIE Laboratory Batna 2 University, Algeria
Abstract: With the explosive growth of Web of Things (WoT)
and social web, it is becoming hard for device owners and users to find
suitable web Application Programming Interface (API) that meet their
needs among a large amount of web APIs. Social-aware and collaborative
filtering-based recommender systems are widely applied to recommend
personalized web APIs to users and to face the problem of information overload.
However, most of the current solutions suffer from the dilemma of accuracy-
diversity where the prediction accuracy gains are typically accompanied by
losses in the diversity of the recommended APIs due to the influence of popularity
factor on the final score of APIs (e.g., high rated or high-invoked APIs). To
address this problem, the purpose of this paper is developing an improved
recommendation model called (Personalized Web API Recommendation) PWR, which
enables to discover APIs and provide personalized suggestions for users without
sacrificing the recommendation accuracy. To validate the performance of our
model, seven variant algorithms of different approaches (popularity-based, user-based
and item-based) are compared using MovieLens 20M dataset. The experiments show
that our model improves the recommendation accuracy by 12% increase with the
highest score among compared methods. Additionally it outperforms the compared
models in diversity over all lengths of recommendation lists. It is envisaged
that the proposed model is useful to accurately recommend personalized web API
for users.
Keywords: Web
of Things, recommender system, web API, collaborative filtering, rating
prediction, social networks, IoT.
Received February 20, 2021; accepted March 7, 2021
https://doi.org/10.34028/iajit/18/3A/7