scholarly journals A Novel Contextual Information Recommendation Model and Its Application in e-Commerce Customer Satisfaction Management

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Feipeng Guo ◽  
Qibei Lu
Keyword(s):  
Customer Satisfaction ◽  
Collaborative Filtering ◽  
Distributed Cognition ◽  
Contextual Information ◽  
User Interest ◽  
User Interests ◽  
Good Ability ◽  
Extraction Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Information Recommendation

In the current supply chain environment, distributed cognition theory tells us that various types of context information in which a recommendation is provided are important for e-commerce customer satisfaction management. However, traditional recommendation model does not consider the distributed and differentiated impact of different contexts on user needs, and it also lacks adaptive capacity of contextual recommendation service. Thus, a contextual information recommendation model based on distributed cognition theory is proposed. Firstly, the model analyzes the differential impact of various sensitive contexts and specific examples on user interest and designs a user interest extraction algorithm based on distributed cognition theory. Then, the sensitive contexts extracted from user are introduced into the process of collaborative filtering recommendation. The model calculates similarity among user interests. Finally, a novel collaborative filtering algorithm integrating with context and user similarity is designed. The experimental results in e-commerce and benchmark dataset show that this model has a good ability to extract user interest and has higher recommendation accuracy compared with other methods.

Time-Weighted Collaborative Filtering Algorithm Based on Improved Mini Batch K-Means Clustering

2021 ◽  
Vol 105 ◽  
pp. 309-317
Author(s):  
Xue Han ◽  
Zhong Wang ◽  
Hui Jun Xu
Keyword(s):  
Collaborative Filtering ◽  
Sparse Matrix ◽  
Pearson Correlation ◽  
Absolute Error ◽  
User Interest ◽  
User Similarity ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Low Efficiency ◽  
Score Matrix

The traditional collaborative filtering recommendation algorithm has the defects of sparse score matrix, weak scalability and user interest deviation, which lead to the low efficiency of algorithm and low accuracy of score prediction. Aiming at the above problems, this paper proposed a time-weighted collaborative filtering algorithm based on improved Mini Batch K-Means clustering. Firstly, the algorithm selected the Pearson correlation coefficient to improve the Mini Batch K-Means clustering, and used the improved Mini Batch K-Means algorithm to cluster the sparse scoring matrix, calculated the user interest score to complete the filling of the sparse matrix. Then, considering the influence of user interest drift with time, the algorithm introduced the Newton cooling time-weighted to improve user similarity. And then calculated user similarity based on the filled score matrix, which helped to get the last predicted score of unrated items The experimental results show that, compared with the traditional collaborative filtering algorithms, the mean absolute error of Proposed improved algorithm is d, and the Precision, Recall and F1 value of MBKT-CF also get a large improvement, which has a higher rating prediction accuracy.

scholarly journals Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Xiushan Zhang
Keyword(s):  
Collaborative Filtering ◽  
Nearest Neighbor ◽  
Absolute Error ◽  
User Model ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Recommendation Algorithms ◽  
Similarity Calculation ◽  
Collaborative Filtering Algorithm ◽  
Recommendation Accuracy

Based on the understanding and comparison of various main recommendation algorithms, this paper focuses on the collaborative filtering algorithm and proposes a collaborative filtering recommendation algorithm with improved user model. Firstly, the algorithm considers the score difference caused by different user scoring habits when expressing preferences and adopts the decoupling normalization method to normalize the user scoring data; secondly, considering the forgetting shift of user interest with time, the forgetting function is used to simulate the forgetting law of score, and the weight of time forgetting is introduced into user score to improve the accuracy of recommendation; finally, the similarity calculation is improved when calculating the nearest neighbor set. Based on the Pearson similarity calculation, the effective weight factor is introduced to obtain a more accurate and reliable nearest neighbor set. The algorithm establishes an offline user model, which makes the algorithm have better recommendation efficiency. Two groups of experiments were designed based on the mean absolute error (MAE). One group of experiments tested the parameters in the algorithm, and the other group of experiments compared the proposed algorithm with other algorithms. The experimental results show that the proposed method has better performance in recommendation accuracy and recommendation efficiency.

scholarly journals A Survey of Collaborative Filtering Recommender Algorithms and Their Evaluation Metrics

2017 ◽  
Vol 2 (2) ◽  
pp. 14 ◽  
Author(s):  
Mahdi Jalili
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Contextual Information ◽  
Previous History ◽  
Rank Aggregation ◽  
Performance Criteria ◽  
Target User ◽  
Collaborative Filtering Algorithm ◽  
Evaluation Metric

Abstract—Recommender systems are often used to provide useful recommendations for users. They use previous history of the users-items interactions, e.g. purchase history and/or users rating on items, to provide a suitable recommendation list for any target user. They may also use contextual information available about items and users. Collaborative filtering algorithm and its variants are the most successful recommendation algorithms that have been applied to many applications. Collaborative filtering method works by first finding the most similar users (or items) for a target user (or items), and then building the recommendation lists. There is no unique evaluation metric to assess the performance of recommendations systems, and one often choose the one most appropriate for the application in hand. This paper compares the performance of a number of well-known collaborative filtering algorithms on movie recommendation. To this end, a number of performance criteria are used to test the algorithms. The algorithms are ranked for each evaluation metric and a rank aggregation method is used to determine the wining algorithm. Our experiments show that the probabilistic matrix factorization has the top performance in this dataset, followed by item-based and user-based collaborative filtering. Non-negative matrix factorization and Slope 1 has the worst performance among the considered algorithms. Keywords—Social networks analysis and mining, big data, recommender systems, collaborative filtering.

Fuzzy trust based collaborative filtering analysis for mobile user preferences

2021 ◽  
pp. 1-7
Author(s):  
Chengfang Tan ◽  
Lin Cui ◽  
Xiaoyin Wu
Keyword(s):  
Collaborative Filtering ◽  
Rapid Development ◽  
Mobile User ◽  
User Preferences ◽  
Data Matrix ◽  
User Interest ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
The Impact ◽  
Fuzzy Trust

With the rapid development of mobile terminal devices, mobile user activities can be carried out anytime and anywhere through various mobile terminals. The current research on mobile communication network is mainly focused on extracting useful and interesting information for mobile user from massive and disordered information. However, the sparsity of scoring data matrix results in low quality of recommendation algorithm. In order to overcome this drawback, the traditional collaborative filtering algorithm is improved. First, the user-interest matrix and item-feature matrix were obtained by analyzing mobile user behavior and item attributes. Fuzzy trust based model is utilized for collaborative filtering analysis for mobile user preferences. Then, the similarity between different mobile users was calculated by weighted calculation. With this method, mobile user preference can be predicted effectively, making it possible to recommend rational resource and waste less time in extracting resources out of the massive information. Experimental results show that the proposed algorithm reduces the mean absolute error (MAE) and the impact of sparse scoring matrix data compared with the traditional collaborative filtering algorithm, and improves the recommendation effect to a certain extent.

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.

A restaurant recommendation approach with the contextual information

2021 ◽  
pp. 1-9
Author(s):  
Lei Chen ◽  
Meimei Xia
Keyword(s):  
Collaborative Filtering ◽  
Contextual Information ◽  
Current Time ◽  
Filtering Algorithm ◽  
Recommendation Algorithms ◽  
Collaborative Filtering Algorithm ◽  
Inherent Problem ◽  
Scoring Matrix ◽  
Time Price ◽  
Potential Customers

Recommender systems can recommend products by analyzing the interests and habits of users. To make more efficient recommendation, the contextual information should be collected in recommendation algorithms. In the restaurant recommendation, the location and the current time of customers should also be considered to facilitate restaurants to find potential customers and give accurate and timely recommendations. However, the existing recommendation approaches often lack the consideration of the influence of time and location. Besides, the data sparsity is an inherent problem in the collaborative filtering algorithm. To address these problems, this paper proposes a recommendation approach which combines the contextual information including time, price and location. Instead of constructing the user-restaurant scoring matrix, the proposed approach clusters price tags and generates the user-price scoring matrix to alleviate the sparsity of data. The experiment on Foursquare dataset shows that the proposed approach has a better performance than traditional ones.

Sign in / Sign up

Export Citation Format

Share Document