پیش‌بینی عملکرد نتایج پرس‌وجو با کمک روش‌های بدون نظارت

در سال ‌های اخیر، استفاده از موتورهای جست‌وجو افزایش روزافزون داشته و نیاز به توسعه روش‌های دقیق‌تر بازیابی و رتبه‌بندی اسناد بیشتر شده‌است؛ درنتیجه پیش بینی عملکرد موتورهای جست‌وجو، یکی از الزامات و چالش‌ های بازیابی اطلاعات محسوب می‌شود. اگر بتوان عملکرد پرس ‌وجوها را پیش از مرحله بازیابی یا بعد از آن تخمین زد، می ‌توان اقدامات خاصی را برای بهبود بازیابی انجام داد. پیش‌بینی عملکرد پرس‌وجو بر تخمین دشواری برآوردن درخواست کاربر برای یک روش بازیابی خاص متمرکز است. این پژوهش، به بررسی عملکرد پرس‌وجو با کمک روش‌های پس از بازیابی می‌پردازد؛ در این راستا از روش‌ های بدون نظارت استفاده می‌شود و به خوشه ‌بندی و اندازه ‌گیری معیارهای مختلف جهت ارزیابی عملکرد پاسخ‌ دهی پرس‌ وجوها می‌ پردازیم؛ درنهایت کار خود را با روش‌ های بدون نظارت موجود در ادبیات این حوزه مقایسه خواهیم کرد. نتایج نشان می‌دهد روش پیشنهادی پژوهش حاضر توانست ضریب اسپیرمن را در مجموعه داده trec dl 2019 و dl-hard به ترتیب 0.009 و 0.163 و در مجموعه داده trec dl 2020 ضریب پیرسون را 0.037 نسبت به بهترین کار موجود افزایش دهد.

drive test route optimization in mobile networks

the design and maintenance of the radio access network (ran) in mobile telecommunications requires meticulous monitoring of network performance to ensure optimal quality and coverage. drive testing is a prevalent methodology for collecting data on network status across delineated geographical areas. this process involves systematically traversing various routes, including streets and pathways, to assess network performance metrics such as reference signal received power (rsrp). although drive testing provides essential insights for identifying regions with inadequate signal quality, it is inherently resource-intensive, involving considerable time and financial expenditures. this paper proposes an innovative optimization methodology aimed at enhancing the efficiency of data collection during drive tests. the proposed approach is organized into four fundamental steps: (1) partitioning the map into smaller sections, (2) selecting critical points within each section, (3) employing map-matching techniques to accurately align these points with actual streets and pathways, and (4) determining the optimal route for traversing the critical points. a rectangular area of interest is selected and divided into k smaller sub-regions, within which m critical points are identified according to a predefined criterion. these points, which may not initially correspond with the existing street network, are corrected through map-matching techniques to ensure feasible traversal paths. lastly, an optimization algorithm is utilized to compute the shortest route that encompasses all identified critical points. the efficacy of the proposed method is assessed through experimental studies that manipulate key parameters k (denoting the number of sub-regions) and m (indicating the maximum critical points per zone). the evaluation emphasizes two critical metrics: the total distance traveled and the success rate in detecting areas with rsrp values below -100 dbm. results indicate that the proposed approach significantly decreases the distance required for drive testing while achieving substantial coverage of areas with weak signals. for example, in an experiment where the optimized route covered only 18.54 kilometers—equivalent to 34% of the distance of an entire drive test—it successfully identified 70% of regions with poor rsrp. furthermore, this paper introduces a criterion for assessing the effectiveness of drive test routes, highlighting the balance between route length and coverage of low-signal areas. the findings substantiate that adequate data for network performance analysis can be secured with considerable savings in both cost and time compared to conventional exhaustive drive testing. while this study concentrates on rsrp measurements within 4g networks, the proposed methodology is adaptable for other metrics, including reference signal received quality (rsrq). future research may also examine the integration of alternative data sources, such as satellite imagery, to further refine map partitioning and critical point selection, thereby enhancing the overall efficacy of the proposed method