تولید توضیح شخصی سازی شده برای سیستم پیشنهاددهنده لیست توئیتر مبتنی بر شباهت معنایی هشتگ ها

چکیده مقاله :

امروزه سیستم‌های پشنهاددهنده ‏ی لیست‏ های توئیتر با بکارگیری اطلاعات مختلف کاربران و لیست‏ها و همچنین اعمال الگوریتم‏های پیچیده، توانسته‏ اند به دقت بالایی در پیش‏ بینی برسند و پیشنهادهای مرتبط با هر کاربر را تولید کنند، اما قابلیت توضیح‏ پذیری در این سیستم ‏ها به عنوان یک چالش مطرح می‏ باشد. توضیح مناسب به همراه یک لیست پیشنهادشده علاوه بر افزایش اعتماد و رضایت کاربران، می‏ تواند به آن‏ها در تصمیم‌گیری آگاهانه کمک نماید. از این رو در این مقاله، یک مدل تولید توضیح ارائه می‏شود که به ایجاد خودکار یک توصیف برای لیست پیشنهادشده بصورت شخصی‏ سازی شده برای کاربر مدنظر می پردازد. بطور دقیق‏تر، این مدل با انتخاب هشتگ‏های پرتکرار از محتوای لیست که ارتباط معنایی با تاریخچه فعالیت‏ های قبلی کاربر دارد، سعی می‏کند موضوع لیست را به گونه‏ای قابل‏ درک و شخصی ‏سازی شده در قالب یک توضیح همراه با لیست پیشنهادشده نمایش دهد. پس از جمع‏ آوری یک مجموعه داده واقعی از شبکه توئیتر، با انجام آزمایش‌ها نشان داده شد که مدل پیشنهادی قادر به تولید توضیح برای درصد بالایی از پیشنهادهای ایجادشده براساس یک مدل پیشنهاددهنده پایه می‏ب اشد.

چکیده انگلیسی :

Twitter List recommender systems have achieved high prediction accuracy by leveraging diverse user and List information alongside complex algorithms. However, explainability remains a significant challenge in these systems. Providing meaningful explanations along with a set of recommendations can enhance user trust and satisfaction, assisting them in informed decision-making. In this paper, we present a model for the automated generation of personalized descriptions as explanations for recommended Twitter Lists. Specifically, our model selects frequently used hashtags from the content of the recommended List, establishing semantic relationships with the user's activity history. The aim is to present the List's subject in an understandable and personalized manner through a generated description. Through experiments conducted on a real Twitter dataset, our proposed model demonstrates its capability to generate explanations for a high percentage of the recommendations provided by a recommendation model.

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