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.

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[29] Zhou T., Kuscsik Z., Liu J. , Medo M., Wakeling J., and Zhang Y., “Solving the Apparent A Personalized Recommendation for Web API Discovery in Social Web of Things 445 Diversity-accuracy Dilemma of Recommender Systems,” Proceedings of the National Academy of Sciences, vol. 107, no. 10, pp. 4511-4515, 2010. Marwa Meissa is a Phd student in intelligent Computer Science Laboratory University Mohamed Khider Biskra, Algeria. Her interests include service composition, recommender system, social computing and Internet of Things. Saber Benharzallah is a professor and researcher in the computer science department of Batna 2 University (Algeria). Received his Ph. D degree in 2010 from the Biskra University (Algeria). Prof. Benharzallah is currently director of laboratory LAMIE (Batna 2 University). His research interests include Internet of things, service-oriented architecture, context aware systems, Social IoT. Laid Kahloul received the Ph.D. degree in computer software and theory from the Computer Science Department, Biskra University, Biskra, Algeria, in 2012. He is currently a professor with the Computer Science Department, Biskra University, Algeria. Okba Kazar professor in the Computer Science Department of Biskra. He is a member of international conference program committees and the "editorial board" for various magazines. His research interests are artificial intelligence, multi-agent systems, web applications and information systems.