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Register a new userCRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs
CRFR: تحسين نظم التوصية المحادثة عبر شظايا مرنة معرفية على الرسوم البيانية المعرفة
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على الرغم من أن مسارات اهتمامات المستخدم التحول في الرسوم البيانية المعرفة (KGS) يمكن أن تستفيد أنظمة التوصية المحادثة (CRS)، فإن المنطق الصريح على KGS لم يتم النظر فيه بشكل جيد في CRS، بسبب مجمع المسارات عالية الجودة وغير كاملة.نقترح CRFR، والتي تقوم بفعالية بتفريغ المنطق متعدد القفزات على KGS مع نموذج تعليمي التعزيز في سياق المحادثة.بالنظر إلى عدم اكتمال KGS، بدلا من تعلم مسار التفكير الكامل الفردي، يتعلم CRFR مرن شظايا التفكير المتعددة المحتمل الواردة في المسارات الكاملة لتحويل المصالح.يتم بعد ذلك مصمما بعد ذلك نموذج موحدة شظايا يدلي بمعلومات الشظايا من KGS الموجهة نحو البند والموجهة نحو المنفاه لتعزيز استجابة CRS مع الكيانات والكلمات من الشظايا.تثبت تجارب واسعة النطاق أداء SOTA الخاص ب CRFR على التوصية والمحادثة وتفسير المحادثة.
Although paths of user interests shift in knowledge graphs (KGs) can benefit conversational recommender systems (CRS), explicit reasoning on KGs has not been well considered in CRS, due to the complex of high-order and incomplete paths. We propose CRFR, which effectively does explicit multi-hop reasoning on KGs with a conversational context-based reinforcement learning model. Considering the incompleteness of KGs, instead of learning single complete reasoning path, CRFR flexibly learns multiple reasoning fragments which are likely contained in the complete paths of interests shift. A fragments-aware unified model is then designed to fuse the fragments information from item-oriented and concept-oriented KGs to enhance the CRS response with entities and words from the fragments. Extensive experiments demonstrate CRFR's SOTA performance on recommendation, conversation and conversation interpretability.
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