User Preference and Reviews Analysis with Neural Networks for Travel Recommender Systems
نویسندگان
چکیده
منابع مشابه
User-Involved Preference Elicitation for Product Search and Recommender Systems
products or services, planning a trip, or scheduling resources, people increasingly rely on computerized product recommender systems (also called product search tools) to find outcomes that best satisfy their needs and preferences. However, automated decision systems cannot effectively search the space of possible solutions without an accurate model of a user’s preferences. Preference acquisiti...
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In recent years, recommender systems have become widely utilized by businesses across industries. Given a set of users, items, and observed user-item interactions, these systems learn user preferences by collective intelligence, and deliver proper items under various contexts to improve user engagements and merchant profits. Collaborative Filtering is the most popular method for recommender sys...
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در این تحقیق شبکه عصبی مصنوعی برای پیش بینی مقادیر ضریب اطمینان و فاکتور ایمنی بحرانی سدهای خاکی ناهمگن ضمن در نظر گرفتن تاثیر نیروی اینرسی زلزله ارائه شده است. ورودی های مدل شامل ارتفاع سد و زاویه شیب بالا دست، ضریب زلزله، ارتفاع آب، پارامترهای مقاومتی هسته و پوسته و خروجی های آن شامل ضریب اطمینان می شود. مهمترین پارامتر مورد نظر در تحلیل پایداری شیب، بدست آوردن فاکتور ایمنی است. در این تحقیق ...
Various aspects of user preference learning and recommender systems
In this paper, we describe area of recommender systems, with focus on user preference learning problem. We describe such system and identify some interesting problems. We will compare how well different approaches cope with some of the problems. This paper may serve as an introduction to the area of user preference learning with a hint on some interesting problems that have not been solved yet.
متن کاملTravel Recommender Systems
Recommender systems are commonly defined as applications that e-commerce sites exploit to suggest products and provide consumers with information to facilitate their decision-making processes.1 They implicitly assume that we can map user needs and constraints, through appropriate recommendation algorithms, and convert them into product selections using knowledge compiled into the intelligent re...
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ژورنال
عنوان ژورنال: International Journal of Engineering Research and Technology
سال: 2020
ISSN: 0974-3154
DOI: 10.37624/ijert/13.8.2020.1896-1900