Recommending systems are systems that, by taking limited information from the user and features such as what the user has searched for in the past and what product they have rated, can correctly identify the user and the desired items Offer the user. The user's desired items are suggested to him through the user profile. In this research, a new method is presented to recommend the user's interests in the form of the user's personalized profile. The way to do this is to use other users' searched information in the form of a database to recommend to new users. The procedure is that we first collect a log file from the items searched by users, then we pre-process this log file to remove the data from the raw state and clean it. Then, using data weighting and using the score function, we extract the most searched items of users in the past and provide them to the user in the form of a recommendation system based on participatory filtering. Finally, we use our data using an algorithm. We optimize the cuckoo that this information can be of interest to the user. The results of this study showed 99% accuracy and 97% frequency, which can to a large extent correctly predict the user's favorite items and pages and start with the problem that is the problem of most recommender systems To confront. تفاصيل المقالة

رشد روزافزون فناوری اطلاعات تحول وسیعی را در فروش کالا و خدمات ایجاد نموده است. با اطلاعات به‌دست‌آمده از مشتریان فروشگاه‌های زنجیره ایی می‌توان از این اطلاعات هم برای بهبود خریدهای آتی این مشتریان هم برای کمک به فروشگاه‌های بزرگ جهت رفع نیاز مشتریان استفاده نمود. برای انجام این کار از روش‌های داده‌کاوی استفاده می‌گردد. تحلیل سبد خرید مشتریان یکی از مهم‌ترین کاربردهای داده کاوی می‌باشد که بر کشف الگوهای خرید به وسیله داده‌های معاملات انجام‌شده، تمرکز می‌کند. در این تحقیق به ارائه روشی به‌منظور بهبود تحلیل سبد خرید مشتریان فروشگاه‌های زنجیره ایی پرداخته می‌شود که تا حد زیادی می‌تواند تحلیل سبد خرید مشتریان را بهبود بخشد. روش پیشنهادی موجود در این تحقیق استفاده از دو الگوریتم قواعد وابستگی و الگوریتم Apriori می‌باشد. طبق تحقیقات انجام‌شده روش پیشنهادی موجود در این تحقیق می‌تواند تا حدود 94% به‌درستی سبد خرید مشتریان را تحلیل کرده و نیازهای مشتریان و فروشگاه‌ها را به‌درستی تشخیص دهد. تفاصيل المقالة

Due to the growing number of videos available on the web, it seems necessary to have a system that can extract users' favorite videos from a huge amount of information that is increasing day by day. One of the best ways to do this is to use referral systems. In this research, a method is provided to improve the recommender systems in the field of film recommendation to the user. In this research, DBSCAN clustering algorithm is used for data clustering. Then we will optimize our data using the cuckoo algorithm, then the genetic algorithm is used to predict the data, and finally, using a recommender system based on participatory refinement, a list of different movies that can be of interest to the user is provided. The results of evaluating the proposed method indicate that this recommender system obtained a score of 99% in the accuracy of the system and a score of 95% in the call section Suggest the user's favorite videos correctly to the user. تفاصيل المقالة