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Register a new userFaithfully Explainable Recommendation via Neural Logic Reasoning
تفسير التوصية بإخلاص عبر المنطق العصبي
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أصبحت الرسوم البيانية المعرفة (KG) من الأهمية بمثابة الأهمية لإيواء أنظمة التوصية الحديثة مع القدرة على توليد مسارات التفكير القابلة للتتبع لشرح عملية التوصية.ومع ذلك، نادرا ما تعتبر البحث المسبق إخلاص التفسيرات المشتقة لتبرير عملية صنع القرار.إلى حد ما من معرفتنا، هذا هو أول عمل نماذج ويقيم التوصية القابلة للتفسير بأمانة في إطار التفكير KG.على وجه التحديد، نقترح المنطق العصبي لتوصية التوصية (LOGER) الشرح (Loger) عن طريق الاستفادة من القواعد المنطقية القابلة للتفسير لتوجيه عملية التفكير في المسار لتوليد التفسير.نقوم بتجربة ثلاثة مجموعات بيانات واسعة النطاق في مجال التجارة الإلكترونية، مما يدل على فعالية طريقتنا في تقديم توصيات عالية الجودة وكذلك التأكد من إخلاص التفسير المشتق.
Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process. However, prior research rarely considers the faithfulness of the derived explanations to justify the decision-making process. To the best of our knowledge, this is the first work that models and evaluates faithfully explainable recommendation under the framework of KG reasoning. Specifically, we propose neural logic reasoning for explainable recommendation (LOGER) by drawing on interpretable logical rules to guide the path-reasoning process for explanation generation. We experiment on three large-scale datasets in the e-commerce domain, demonstrating the effectiveness of our method in delivering high-quality recommendations as well as ascertaining the faithfulness of the derived explanation.
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