scholarly journals Improved Personalized Recommendation Algorithm Based on Context-Aware in Mobile Computing Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Fei Long
Keyword(s):  
Mobile Computing ◽  
Personalized Recommendation ◽  
Mobile Service ◽  
Computing Environment ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Related Factors ◽  
Mobile Computing Environment ◽  
Collaborative Filtering Algorithm ◽  
User Interest Model

With the development and popularization of e-commerce and Internet, more and more attention has been paid to personalized recommendation for users. The traditional user interest model only considers the user’s behavior on the project, ignoring the user’s context at that time. Pointing to the shortage that context-related factors are not considered in previous works, combining the characteristics of a mobile computing environment, this paper studies the algorithm and model of mobile service recommendation. A recommendation algorithm based on specified context filtering in mobile computing environment is proposed. The context of the classification is aggregated, by grouping the scenarios of the same category together. Through experiments, we found that the improved personalized recommendation algorithms are superior to the common collaborative filtering algorithm.

scholarly journals A Personalized Matching System for Management Teaching Resources Based on Collaborative Filtering Algorithm

2020 ◽  
Vol 15 (13) ◽  
pp. 207
Author(s):  
Yongqing Shi ◽  
Xiaojiang Yang
Keyword(s):  
Collaborative Filtering ◽  
Teaching Quality ◽  
Personalized Recommendation ◽  
User Interest ◽  
Teaching Resources ◽  
Collaborative Filtering Algorithm ◽  
User Interest Model ◽  
Set Up ◽  
Learning Interest ◽  
Opening Up

To realize education informatization, it is highly necessary to recommend teaching resources to students that can enhance their learning interest and improve teaching quality. This paper develops a personalized matching system for management teaching resources based on collaborative filtering (CF) algorithm. Firstly, the authors set up a user interest model, designed the flow and algorithm for personalized matching, and improved the similarity calculation method. Next, a personalized recommendation algorithm was developed based on the CF, and a personalized matching engine was constructed with the aid of Apache Mahout. The experimental results show that the proposed CF algorithm can effectively improve the recommendation quality, and push personalized teaching resources to each user; the learners are highly satisfied with the personalized matching system. The research results shed new light on personalized recommendation of teaching resources, opening up a new way to education informatization.

The Improved Collaborative Filtering Recommendation Algorithm Based on Cloud Model

2013 ◽  
Vol 411-414 ◽  
pp. 2292-2296
Author(s):  
Jia Si Gu ◽  
Zheng Liu
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Cloud Model ◽  
Personalized Recommendation ◽  
Test Results ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Model Based ◽  
Collaborative Filtering Algorithm

The traditional collaborative filtering algorithm has a better recommendation quality and efficiency, it has been the most widely used in personalized recommendation system. Based on the traditional collaborative filtering algorithm,this paper considers the user interest diversity and combination of cloud model theory.it presents an improved cloud model based on collaborative filtering recommendation algorithm.The test results show that, the algorithm has better recommendation results than other kinds of traditional recommendation algorithm.

scholarly journals A Recommendation Approach for Rating Prediction Based on User Interest and Trust Value

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hailong Chen ◽  
Haijiao Sun ◽  
Miao Cheng ◽  
Wuyue Yan
Keyword(s):  
Collaborative Filtering ◽  
Personalized Recommendation ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Weighting Method ◽  
Trust Value ◽  
Recommendation Algorithms ◽  
Similarity Calculation ◽  
Collaborative Filtering Algorithm ◽  
Recommendation Accuracy

Collaborative filtering recommendation algorithm is one of the most researched and widely used recommendation algorithms in personalized recommendation systems. Aiming at the problem of data sparsity existing in the traditional collaborative filtering recommendation algorithm, which leads to inaccurate recommendation accuracy and low recommendation efficiency, an improved collaborative filtering algorithm is proposed in this paper. The algorithm is improved in the following three aspects: firstly, considering that the traditional scoring similarity calculation excessively relies on the common scoring items, the Bhattacharyya similarity calculation is introduced into the traditional calculation formula; secondly, the trust weight is added to accurately calculate the direct trust value and the trust transfer mechanism is introduced to calculate the indirect trust value between users; finally, the user similarity and user trust are integrated, and the prediction result is generated by the trust weighting method. Experiments show that the proposed algorithm can effectively improve the prediction accuracy of recommendations.

Experience with Disconnected Operation in a Mobile Computing Environment

1993 ◽  
Author(s):  
M. Satyanarayanan ◽  
James J. Kistler ◽  
Lily B. Mummert ◽  
Maria R. Ebling ◽  
Puneet Kumar
Keyword(s):  
Mobile Computing ◽  
Computing Environment ◽  
Disconnected Operation ◽  
Mobile Computing Environment

A Webpage Ranking Algorithm Based on Collaborative Recommendation

2013 ◽  
Vol 765-767 ◽  
pp. 998-1002
Author(s):  
Shao Xuan Zhang ◽  
Tian Liu
Keyword(s):  
Sorting Algorithm ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Search Results ◽  
Collaborative Recommendation ◽  
Interest Model ◽  
Sorting Efficiency ◽  
User Interest Model ◽  
Relevant Calculation ◽  
Personalized Ranking

In view of the present personalized ranking of search results user interest model construction difficult, relevant calculation imprecise problems, proposes a combination of user interest model and collaborative recommendation algorithm for personalized ranking method. The method from the user search history, including the submit query, click the relevant webpage information to train users interest model, then using collaborative recommendation algorithm to obtain with common interests and neighbor users, on the basis of these neighbors on the webpage and webpage recommendation level associated with the users to sort the search results. Experimental results show that: the algorithm the average minimum precision than general sorting algorithm was increased by about 0.1, with an increase in the number of neighbors of the user, minimum accuracy increased. Compared with other ranking algorithms, using collaborative recommendation algorithm is helpful for improving webpage with the user interest relevance precision, thereby improving the sorting efficiency, help to improve the search experience of the user.

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