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Mouadh Guesmi, Mohamed Amine Chatti, Shoeb Joarder, Qurat Ul Ain, Clara Siepmann, Hoda Ghanbarzadeh and Rawaa Alatrash
Significant attention has been paid to enhancing recommender systems (RS) with explanation facilities to help users make informed decisions and increase trust in and satisfaction with an RS. Justification and transparency represent two crucial goals in e...
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Muzamil Hussain Syed, Tran Quoc Bao Huy and Sun-Tae Chung
With the rapid growth of internet data, knowledge graphs (KGs) are considered as efficient form of knowledge representation that captures the semantics of web objects. In recent years, reasoning over KG for various artificial intelligence tasks have rece...
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Hasan Abu-Rasheed, Christian Weber, Johannes Zenkert, Mareike Dornhöfer and Madjid Fathi
In modern industrial systems, collected textual data accumulates over time, offering an important source of information for enhancing present and future industrial practices. Although many AI-based solutions have been developed in the literature for a do...
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Laith T. Khrais
The advent and incorporation of technology in businesses have reformed operations across industries. Notably, major technical shifts in e-commerce aim to influence customer behavior in favor of some products and brands. Artificial intelligence (AI) comes...
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Qingyao Ai, Vahid Azizi, Xu Chen and Yongfeng Zhang
Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms?especially the collaborative filtering (CF)- based approaches with shallow or deep models?usually work with various ...
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