توسعه یک سیستم توصیه‌کننده چند عاملی برای دستیاران خرید هوشمند
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمند
رمضان تیموری یانسری
1
,
مجتبی آجودانی
2
1 - استاديار،گروه مهندسي کامپيوتر، واحد بندرگز، دانشگاه آزاد اسلامي، بندرگز، ايران
2 - استاديار،گروه مهندسي برق، واحد بندرگز، دانشگاه آزاد اسلامي، بندرگز، ايران
کلید واژه: سیستمهای توصیه‌کننده, دستیار خرید هوشمند, سیستمهای چند عاملی, یادگیری ماشین, عامل‌های هوشمند.,
چکیده مقاله :
با توجه به حجم فزاینده اطلاعات و خدمات موجود در وب، ارائه ابزارهایی مانند سیستمهای توصیه‌کننده به وب‌سایت‌ها و برنامه‌های کاربردی که می‌توانند به کاربران در دستیابی به اطلاعات و خدمات متناسب با علایقشان کمک کنند، ضروری است. به همین دلیل، ارائه راهنمایی و پیشنهاد مناسب به کاربران در انتخاب‌های مختلف، مطابق با اولویت‌های کاربر در حوزه‌های مختلف جایگاه خاصی پیدا کرده است. سامانه‌های توصیه کننده سیستمهای اطلاعاتی هستند که با مدل‌سازی رفتار کاربران در محیط‌های عملیاتی در رتبه‌بندی، مقایسه، انتخاب و ترجیحات اقلام کاربران، با محدود کردن فضای جستجوی از طریق توصیه‌های با دقت و کیفیت بالا، در فرآیند تصمیم‌گیری کمک می‌کنند. در این پژوهش سیستم توصیه کننده چند عاملی پیشنهاد شد که بتواند به عنوان دستیار خرید در فرآیند خرید توصیه-های مناسبی ارائه دهد. برای تحلیل مدل پیشنهادی مجموعه داده فروش یک فروشگاه مستقر در بریتانیا شامل 1067371 رکورد از داده-های فروش آنلاین، مورد استفاده قرار گرفته است. با شبیه سازی مدل پیشنهادی نتایج حاصل از به کارگیری مدل بر روی داده‌های مربوط به مشتریان مورد تجزیه و تحلیل قرار گرفت. نتایج به کارگیری مدل پیشنهادی نشان داد، مدل پیشنهادی در ارزیابی پارامترهای مورد استفاده در مقایسه با روش‌های رایج در این حوزه دارایی کارایی مناسبی می‌باشد.
Introduction: Due to the increasing volume and services available on the web, tools such as recommender systems in websites and applications that can help users find information and services of interest can be provided. For this reason, suitable guidance and suggestions for users in different choices, according to the user's priorities in different areas of a specific position, have been provided.
Method: Recommender systems are information systems that assist in the decision-making process by modeling the behavior of users in operational environments in ranking, comparing, selecting and choose items by users, narrowing the information search through high-quality and accurate recommendations. In this research, a multi-agent recommender system is proposed as an intelligent shopping assistant in the process of buying suitable offers. The proposed model is used to analyze the sales data set of a UK-based store containing 1,067,371 records of online sales data.
Results: By simulating the proposed model, the results of applying the model to the relevant data were analyzed. The proposed model in this research was simulated in MATLAB software version 2022 and the results of applying the proposed model on the data related to the sale of an online shopping were analyzed. According to the results, in this evaluation, the accuracy of the proposed model was 91.5% on average, compared to the neural network model, it was 86.41%, compared to the KNN model, 78.32%, compared to the SOM Ensembles model, 74.38%, compared to the Global Top-N model, 69.78%, compared to the Weighted item-based model, 72.31%, and compared to The Naïve Bayesian model has an accuracy of 59.68%, a higher accuracy in the right suggestion to users.
Discussion: In this research, while studying recommender systems, the challenges in this field were examined and multi-agent systems were used to provide suggestions and recommendations with high accuracy and quality in ranking, comparison, selection and preferences of users' items in the decision-making process in operational environments. By combining multi-agent systems, multi-agent recommender systems were proposed that can provide suitable recommendations as a purchasing assistant in the purchasing process. The results of applying the proposed model on the data related to the purchase history of the customers of an online shopping showed that the proposed model has a good efficiency in evaluating the parameters used in comparison with the common methods in this property field.
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