Evolutionary User Clustering Based on Time-Aware Interest Changes in the Recommender System
نویسندگان
چکیده مقاله:
The plenty of data on the Internet has created problems for users and has caused confusion in finding the proper information. Also, users' tastes and preferences change over time. Recommender systems can help users find useful information. Due to changing interests, systems must be able to evolve. In order to solve this problem, users are clustered that determine the most desirable users, it pays attention to the user's rating of the items. The time parameter has been considered in the proposed method of Genetic Algorithm-Simulated Algorithm (SAGA) of this paper which can improve user prioritization based on time. In the proposed method, using the Memetic evolutionary algorithm, the clusters are improved over time, which it provides appropriate suggestions to the user. The system also performs optimal evolutionary clustering using item properties for the cold start item problem, and user demographic information for the cold start user problem. The proposed method has been evaluated using the Movielens dataset and experimental results show that the proposed SAGA method with an accuracy of 0.89 has a better performance in the accuracy of predictions and suggestions to users than existing methods.
منابع مشابه
A Job Recommender System Based on User Clustering
In this paper, we first provide a comprehensive investigation of four online job recommender systems (JRSs) from four different aspects: user profiling, recommendation strategies, recommendation output, and user feedback. In particular, we summarize the pros and cons of these online JRSs and highlight their differences. We then discuss the challenges in building high-quality JRSs. One main chal...
متن کاملSocial Clustering-based Similar User Indexing for Large Recommender System
The tremendous growth of data in recent years poses some key challenges for recommender systems. Theses keys are related with producing high quality recommendations and fast performing the composition recommended items. In this paper, we propose social clustering-based similar user index to not only improve the prediction of recommendations, but also compose personalized recommendations in fast...
متن کاملA social recommender system based on matrix factorization considering dynamics of user preferences
With the expansion of social networks, the use of recommender systems in these networks has attracted considerable attention. Recommender systems have become an important tool for alleviating the information that overload problem of users by providing personalized recommendations to a user who might like based on past preferences or observed behavior about one or various items. In these systems...
متن کاملRecommender System Based on User-generated Content
Recommender systems apply statistical and knowledge discovery techniques to the problem of making recommendations during live user interaction. This paper describes a novel approach of building recommender systems for the Web with the aid of usergenerated content. Recently certain communities of Internet users have engaged in creating high quality peer reviewed content for the Web. In our appro...
متن کاملA time-aware spatio-textual recommender system
Location-Based Social Networks (LBSNs) allow users to post ratings and reviews and to notify friends of these posts. Several models have been proposed for Point-of-Interest (POI) recommendation that use explicit (i.e. ratings, comments) or implicit (i.e. statistical scores, views, and user influence) information. However the models so far fail to capture sufficiently user preferences as they ch...
متن کاملSkewness-aware Clustering Social Recommender
Recommender systems have been a hot research area recently. One of the most widely used methods is Collaborative Filtering(CF), which selects items for an individual user from other similar users. However, CF may not fully reflect the procedure of how people choose an item in real life, for users are more likely to ask friends for opinions instead of asking strangers with similar interests. Rec...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ذخیره در منابع من قبلا به منابع من ذحیره شده{@ msg_add @}
عنوان ژورنال
دوره 19 شماره 3
صفحات 1- 15
تاریخ انتشار 2022-09
با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.
کلمات کلیدی برای این مقاله ارائه نشده است
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023