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Register a new user``It doesn't look good for a date'': Transforming Critiques into Preferences for Conversational Recommendation Systems
"إنه لا يبدو جيدا لتاريخ": تحويل الانتقادات إلى تفضيلات نظم توصية المحادثة
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المحادثات التي تهدف إلى تحديد التوصيات الجيدة هي تكرار الطبيعة. غالبا ما يعبر الناس عن تفضيلاتهم من حيث نقد التوصية الحالية (على سبيل المثال، لا يبدو جيدا لتاريخ "")، مما يتطلب درجة من الحس السليم للحصول على تفضيل يستنتج. في هذا العمل، نقدم طريقة لتحويل نقد المستخدم إلى تفضيل إيجابي (E.G.، أفضل المزيد من الرومانسية ") من أجل استرداد المراجعات المتعلقة بالتوصيات التي يحتمل أن تكون أفضل (على سبيل المثال، مثالية لعشاء رومانسي"). نستفيد نموذجا كبيرا باللغة العصبية (LM) في بيئة قليلة لإجراء تحول من النقد إلى التفضيل، ونحن نختبر طريقتين لاسترداد التوصيات: واحد يطابق المضبوطات، وآخر أن يضغط غرامة على المهمة وبعد نحن نبذ هذا النهج في مجال المطعم وتقييمه باستخدام مجموعة بيانات جديدة من انتقادات المطعم. في دراسة الاجتثاث، نوضح أن استخدام التحول في النقد إلى تحسين التوصيات يحسن التوصيات، وأن هناك ثلاثة قضايا عامة على الأقل تفسر هذا الأداء المحسن.
Conversations aimed at determining good recommendations are iterative in nature. People often express their preferences in terms of a critique of the current recommendation (e.g., It doesn't look good for a date''), requiring some degree of common sense for a preference to be inferred. In this work, we present a method for transforming a user critique into a positive preference (e.g., I prefer more romantic'') in order to retrieve reviews pertaining to potentially better recommendations (e.g., Perfect for a romantic dinner''). We leverage a large neural language model (LM) in a few-shot setting to perform critique-to-preference transformation, and we test two methods for retrieving recommendations: one that matches embeddings, and another that fine-tunes an LM for the task. We instantiate this approach in the restaurant domain and evaluate it using a new dataset of restaurant critiques. In an ablation study, we show that utilizing critique-to-preference transformation improves recommendations, and that there are at least three general cases that explain this improved performance.
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In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2.0) to enable researchers to explore a challenging task of multilingual and cross-lingual conversational recommendation. The difference between Du
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