A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context

Abstract

Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user’s behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user’s interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time.

Various Implementation of Collaborative Filtering-Based Approach on Recommendation Systems using Similarity

“…The rapid growth of information on the internet allows one to obtain more information and have a variety of choices via the Internet, which provides comfort and convenience for people's daily lives. However, the amount of information available often makes a person confused and difficult to make the right choice [1] [2]. Therefore, the recommendation system can simplify the process of selecting information, the recommendation system can automatically suggest information or items that users might like [3].…”

“…However, the amount of information available often makes a person confused and difficult to make the right choice [1] [2]. A traditional search engine can ease the requirements of user information retrieval to some extent [1]. Even so, search engines can only present the same sorting results to all users and cannot provide personalized services according to different user interests.…”

“…Recommendation systems are becoming more and more popular with the onset of the World Wide Web and big data [4]. Specifically, according to various data collection and analysis of user preferences, a personalized recommendation system studies the interests and patterns of user behavior to recommend an information and service needed for the user [1].…”

User Profile Correlation-Based Similarity Algorithm in Movie Recommendation System

“…The exponential growth of information on the internet causes users can get more information resources to dig up and collect. However, users will get lost in the sea of information and will have difficulty in processing that information [1,2]. Users have to spend more time and more energy finding the information they want, but users may not necessarily get satisfactory results.…”

“…Meanwhile, the memory-based method uses the rating database to calculate the similarity between users or similarity between items [24].In its implementation, this method is divided into two techniques, namely User-Based Collaborative Filtering (UBCF) and Item-Based Collaborative Filtering (IBCF) [1,2,25]. The UBCF predicts ratings for all items that have not been rated based on the similarity of users, while the IBCF predicts ratings based on the similarity of items [26].Some of the frequently used traditional similarities are the Cosine (COS), Pearson Correlation Coefficient (PCC), and Jaccard [2,8].…”