یادگیری رتبه بندی محتوای فارسی وب بر مبنای برنامه نویسی ژنتیک چند لایه
محورهای موضوعی : عمومى
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کلید واژه: یادگیری رتبه بندی, مدل برنامه نویسی ژنتیک چند لایه, ویژگی های کلیک از گذر داده, محتوای فارسی وب, مجموعه داده dotIR,
چکیده مقاله :
یادگیری رتبهبندی، یک رویکرد نو ظهور به منظور رفع چالشهای موجود و بهبود عملکرد جویشگرهای وب، بسیار امید بخش و کارآمد است. در عین حال عدم توجه جدی به سوابق تعاملات کاربران با جویشگر طی فرآیند جستجو و ارزیابی نتایج بدست آمده، یکی از معضلات جدی آن بشمار میرود. در عین حال حجم بسیار زیاد ویژگیهای مورد نیاز از اسناد و پرسوجوهای کاربران نیز کاربردی بودن این رویکرد را در شرایط واقعی با ابهام مواجه ساخته است. استفاده از مدل اطلاعات کلیک از گذر دادهها و تولید ویژگیهای کلیک از گذر داده، راهکار نوینی است که بر مبنای آن و با بکارگیری مدل برنامهنویسی ژنتیک چند لایه، مدل رتبهبندی مناسبی تحت عنوان MGP-Rank برای بازیابی اطلاعات انگلیسی وب، عرضه شده است. در این پژوهش این، با عنایت به ویژگیهای خاص زبان فارسی، از طریق ارائه سناریوهای مناسب برای ایجاد ویژگیهای کلیک از گذر داده این الگوریتم، این الگوریتم بومیسازی شده است. نتایج حاصل از ارزیابی عملکرد این الگوریتم در حوزه زبان فارسی با استفاده از مجموعه داده dotIR، حاکی از توانمندی قابل ملاحظه آن نسبت به روشهای مرجع رتبهبندی اطلاعات است. این بهبود عملکرد، بخصوص در بخش ابتدایی فهرست نتایج جستجو که غالباً بیشتر مورد مراجعه کاربران است، قابل توجه است.
Learning to rank (L2R) has emerged as a promising approach in handling the existing challenges of Web search engines. However, there are major drawbacks with the present learning to rank techniques. Current L2R algorithms do not take into account to the search behavior of the users embedded in their search sessions’ logs. On the other hand, machine-learning as a data-intensive process requires a large volume of data about users’ queries as well as Web documents. This situation has made the usage of L2R techniques questionable in the real-world applications. Recently, by the use of the click-through data model and based on the generation of click-through features, a novel approach is proposed, named as MGP-Rank. Using the layered genetic-programming model, MGP-Rank has achieved noticeable performance on the ranking of the English Web content. In this study, with respect to the specific characteristics of the Persian language, some suitable scenarios are presented for the generation of the click-through features. In this way, a customized version of the MGP-Rank is proposed of the Persian Web retrieval. The evaluation results of this algorithm on the dotIR dataset, indicate its considerable improvement in comparison with major ranking methods. The improvement of the performance is particularly more noticeable in the top part of the search results lists, which are most frequently visited by the Web users.
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