Although commercial recommendation system has made certain achievement in travelling route development, the recommendation system is facing a series of challenges because of people’s increasing interest in travelling. It is obvious that the core content of the recommendation system is recommendation algorithm. The advantages of recommendation algorithm can bring great effect to the recommendation system. Based on this, this paper applies traditional collaborative filtering algorithm for analysis. Besides, illustrating the deficiencies of the algorithm, such as the rating unicity and rating matrix sparsity, this paper proposes an improved algorithm combing the multi-similarity algorithm based on user and the element similarity algorithm base...
In this paper, we propose a method to improve the accuracy of item-based collaborative filtering rec...
Since the user recommendation complex matrix is characterized by strong sparsity, it is difficult to...
© 2015 Wiley Periodicals, Inc. Collaborative filtering (CF) is the most popular approach in personal...
Although commercial recommendation system has made certain achievement in travelling route developme...
The recommender system is widely used in the field of e-commerce and plays an important role in guid...
Collaborative filtering is one of the most frequently used techniques in personalized recommendation...
Abstract The interaction and sharing of data based on network users make network information overexp...
This paper presented a new similarity method to improve the accuracy of traditional Collaborative Fi...
Abstract—Similarity method is the key of the user-based collaborative filtering recommend algorithm....
The calculation of user similarity was optimized. By adding the user interest bias as weight into th...
AbstractCollaborative filtering has become one of the most used approaches to provide personalized s...
The explosive growth of the world-wide-web and the emergence of e-commerce has led to the developmen...
This paper discussed the most commonly used similarity measures in Collaborative Filtering (CF) reco...
In the tourism recommendation system, the number of users and items is very large. But traditional r...
With the explosive growth of information resources in the age of big data, mankind has gradually fal...
In this paper, we propose a method to improve the accuracy of item-based collaborative filtering rec...
Since the user recommendation complex matrix is characterized by strong sparsity, it is difficult to...
© 2015 Wiley Periodicals, Inc. Collaborative filtering (CF) is the most popular approach in personal...
Although commercial recommendation system has made certain achievement in travelling route developme...
The recommender system is widely used in the field of e-commerce and plays an important role in guid...
Collaborative filtering is one of the most frequently used techniques in personalized recommendation...
Abstract The interaction and sharing of data based on network users make network information overexp...
This paper presented a new similarity method to improve the accuracy of traditional Collaborative Fi...
Abstract—Similarity method is the key of the user-based collaborative filtering recommend algorithm....
The calculation of user similarity was optimized. By adding the user interest bias as weight into th...
AbstractCollaborative filtering has become one of the most used approaches to provide personalized s...
The explosive growth of the world-wide-web and the emergence of e-commerce has led to the developmen...
This paper discussed the most commonly used similarity measures in Collaborative Filtering (CF) reco...
In the tourism recommendation system, the number of users and items is very large. But traditional r...
With the explosive growth of information resources in the age of big data, mankind has gradually fal...
In this paper, we propose a method to improve the accuracy of item-based collaborative filtering rec...
Since the user recommendation complex matrix is characterized by strong sparsity, it is difficult to...
© 2015 Wiley Periodicals, Inc. Collaborative filtering (CF) is the most popular approach in personal...