طرّاحی سیستم پیشنهاددهنده ترکیبی برای حل مشکل شروع سرد به کمک خوشه‌بندی و الگوریتم‌ ازدحام ذرّات آشوبی

نویسندگان

1 دانشگاه بین المللی امام رضا(ع)

2 دانشگاه صنعتی سجاد

چکیده

امروزه یکی از مهم‌ترین چالش‌های افزایش اطلاعات، یافتن اطلاعات مورد علاقه از بین انبوه داده‌هاست. به این موضوع در طراحی سایت‌های تعاملی همواره توجّه شده‌است. سیستم‌های پیشنهاددهنده برای حل این مسئله به وجود آمده‌اند تا به کاربران برای رسیدن به اطلاعات مورد نظرشان کمک کنند؛ امّا این سیستم‌ها محدویت‌هایی دارند. یکی از مهم‌ترین چالش‌های پیش‌روی سیستم‌های پیشنهاددهنده، مشکل شروع سرد است. این مشکل زمانی به وجود می‌آید که یک کاربر (قلم‌داده) جدید وارد سیستم می‌شود. عدم وجود اطلاعات قبلی از این کاربر (قلم‌داده) باعث می‌شود سیستم نتواند به طور عادی پیشنهادها را تولید کند. در این مقاله برای حل مشکل شروع سرد کاربر، روش جدیدی به کمک ترکیب مدل‌های مبتنی برمحتوا و فیلترمشارکتی ارائه شده است. در این روش لیست پیشنهادی، دارای ویژگی‌هایی مانند کیفیت بالای قلم‌داده‌های پیشنهادی و تنوّع ‌آن‌ها است که دامنه‌ی اطلاعات دریافتی از کاربر را به سرعت گسترش می‌دهد، به همین دلیل کاربران را سریع‌تر از حالت شروع سرد خارج می‌کند. همچنین با استفاده از اطلاعات دموگرافیک کاربر، سعی شده قلم‌داده‌های لیست پیشنهادی به نحوی انتخاب شوند که به علایق کاربر نزدیک‌تر باشند تا دقت بیشتر شود.  نتایج ارزیابی روش پیشنهادی نشان می‌دهد میزان خطای MAE و RMSE نسبت به روش‌های موجود تا حد مطلوبی کاهش یافته‌است.
 

کلیدواژه‌ها


عنوان مقاله [English]

Design a Hybrid Recommender System Solving Cold-start Problem Using Clustering and Chaotic PSO Algorithm

نویسندگان [English]

  • Nafisah Moosarrezayi 1
  • Javad Hamidzadeh 2
چکیده [English]

One of the main challenges of increasing information in the new era, is to find information of interest in the mass of data. This important matter has been considered in the design of many sites that interact with users. Recommender systems have been considered to resolve this issue and have tried to help users to achieve their desired information; however, they face limitations. One of the most important challenges that they face is cold-start problem, which is raised when a new user/item entered into the system, while no previous information is available for it. The lack of previous knowledge of the new user/item, will causes the system fails generating its suggestions normally. In this paper, to solve the problem of cold-start user/item a new method is presented using combining content-based models and collaborative filtering. Moreover, demographic data is used to recommend the nearest items to cold-start users/items' interests. Compared to existing methods, the evaluation results show that the proposed method reduces the MAE and RMSE error.

کلیدواژه‌ها [English]

  • Recommender systems
  • Cold-Start Problem
  • Content-Based Method
  • Collaborative Filtering Method
  • Quality
  • Diversity
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