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

Abstract

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.

Research on the differentiation of residents’ cultural consumption tendency and consumption recommendation system based on network inference algorithm

“…( 2),k(x i ) represents the degree of nodex i anda il is an adjacency matrix ofn * m, whose expression is given in Eq. (3).…”

“…However, the performance of recommendation systems needs to be improved urgently because most of the existing recommendation systems use collaborative ltering and the accuracy of recommendation systems is not high (Zheng G et al 2020) [2]. In recent years, network inference algorithms have started to become popular in the eld of recommendation due to their higher accuracy compared to collaborative ltering techniques (Han X et al 2021) [3]. Traditional network inference algorithms are also used in consumer recommendation systems, but even the performance of recommendation systems that apply traditional network inference algorithms does not reach the expected results (Fukushima A et al 2018) [4].…”

Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model

“…is is the standard optimization method recently adopted to train deep neural networks, and it is easy to implement [22]. At each iteration, the objective function is run on a subset of the training set, which is called the mini-batch.…”

Tourist Attraction Recommendation Method Based on Megadata and Artificial Intelligence Algorithm

“…Han et al subdivided the collaborative filtering algorithm into two types: one type is the collaborative filtering algorithm based on high similarity, and the other is the collaborative filtering algorithm based on the data model. The collaborative filtering method based on the high similarity of neighbors focused on analyzing the relationship between users and entities; the model-based collaborative filtering algorithm learned the prediction model through the user's rating of the entity or other behaviors, built the feature association between the user and the entity, and recommended the entity with high user preference to the users [2]. Many scientists had studied collaborative filtering algorithms based on data models, such as Ngaffo AN's research on matrix factorization factor models.…”

“…The advantage of overlapping techniques is to allow users to feedback their behavior and ratings in social networks, belonging to multiple clusters simultaneously [7]. (2) The problem of sparsity: Chen et al combines the evaluation of individual cognitive behaviors, user cognitive relationships, and time decay coefficients into a probability matrix decomposed by a single model, combined with the social interaction coefficient for personalized recommendation, for the use of social interaction factor for personalized recommendations [8]. Rodpysh et al pointed out that the reason for the sparse data is that most users have a very limited number of reviews for the product.…”