Traditional Collaborative Filtering Recommendation Algorithm Research Articles

Optimization of intelligent recommendation of innovation and entrepreneurship projects based on collaborative filtering algorithm

The advent of the information age has made accurate search for information a challenge. In this paper, we analyze intelligent recommendations for innovative entrepreneurial projects based on collaborative filtering algorithms. Collaborative filtering is one of the most widely used and successful techniques in recommendation systems. In this paper, an interest migration function plus time is introduced to address the shortcomings of traditional collaborative filtering recommendation algorithms. Meanwhile, this paper builds an intelligent recommendation engine system for innovative entrepreneurial projects based on the Hadoop open-source distributed computing framework, sustainable PSCM, and Mahout collaborative filtering recommendation engine technology. This paper uses experiments to test and evaluate the overall performance of the distributed recommendation platform and the improved collaborative filtering recommendation algorithm. It is found that the algorithm outperforms similar algorithms in terms of data volume and coverage of recommended innovation and entrepreneurship projects. This is sufficient to show that the collaborative filtering algorithm and sustainable PSCM are useful for the intelligent recommendation analysis of innovative entrepreneurial projects.

Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

With the popularity of 5G and the rapid development of mobile terminals, an endless stream of short video software exists. Browsing short-form mobile video in fragmented time has become the mainstream of user’s life. Hence, designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements. Nevertheless, the explosive growth of data leads to the low efficiency of the algorithm, which fails to distill users’ points of interest on one hand effectively. On the other hand, integrating user preferences and the content of items urgently intensify the requirements for platform recommendation. In this paper, we propose a collaborative filtering algorithm, integrating time context information and user context, which pours attention into expanding and discovering user interest. In the first place, we introduce the temporal context information into the typical collaborative filtering algorithm, and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos. There remains one more point. We also introduce the user situation into the traditional collaborative filtering recommendation algorithm, considering the context information of users in the generation recommendation stage, and weight the recommended short-form videos of candidates. At last, a diverse approach is used to generate a Top-K recommendation list for users. And through a case study, we illustrate the accuracy and diversity of the proposed method.

A Graph Convolution Collaborative Filtering Integrating Social Relations Recommendation Method

Traditional collaborative filtering recommendation algorithms only consider the interaction between users and items leading to low recommendation accuracy. Aiming to solve this problem, a graph convolution collaborative filtering recommendation method integrating social relations is proposed. Firstly, a social recommendation model based on graph convolution representation learning and general collaborative filtering (SRGCF) is constructed; then, based on this model, a social relationship recommendation algorithm (SRRA) is proposed; secondly, the algorithm learns the representations of users and items by linear propagation on the user–item bipartite graph; then the user representations are updated by learning the representations with social information through the neighbor aggregation operation in the social network to form the final user representations. Finally, the prediction scores are calculated, and the recommendation list is generated. The comparative experimental results on four real-world datasets show that: the proposed SRRA algorithm performs the best over existing baselines on Recall@10 and NDCG@10; specifically, SRRA improved by an average of 4.40% and 9.62% compared to DICER and GraphRec, respectively, which validates that the proposed SRGCF model and SRRA algorithm are reasonable and effective.

Discrete Dynamic Modeling Analysis Based on English Learning Motivation

With the popularization of the Internet, various online learning platforms have developed rapidly, providing users with abundant learning resources, and realizing personalized resource recommendation has become the development trend of online learning platforms. In this paper, a personalized learning recommendation model based on improved collaborative filtering is proposed. Firstly, a multilayer interest model of learners is established to accurately describe learners’ interest in knowledge topics, courses, and knowledge areas; then, in view of the sparse scoring matrix and cold-start problems of traditional collaborative filtering recommendation algorithms, an improved collaborative filtering-based personality is proposed. The personalized learning recommendation model is used to improve the similarity calculation of users by introducing user initialization tags and solve the cold-start problem of new users. Finally, the effectiveness of the algorithm is proved by experimental comparison, and the improved algorithm improves the recommendation effect of personalized learning.

Analysis of the Influence of Hybrid Personalized Recommendation Algorithm on the Employment Tendency of Hotel Management Major

As the pillar industry of tourism, the hotel industry plays an important role in the present and future development of tourism. Therefore, the industry’s demand for hotel management professionals is increasing. In this paper, the mixed personalized recommendation algorithm is used to carry out descriptive statistical analysis on the results, and the characteristics of the employment tendency of college students majoring in hotel management are preliminarily understood. In addition, the influence of human capital and social capital on college students’ employment tendency is discussed. The results show that, through experiments, the hybrid personalized recommendation algorithm proposed in this paper is compared with two traditional collaborative filtering recommendation algorithms based on users, Pearson similarity and cosine similarity. The algorithm in this paper has smaller MAE, which shows that the algorithm proposed in this paper has higher recommendation quality. Through this research, it is helpful to have a more comprehensive understanding of the actual situation and characteristics of the employment tendency of hotel management major college students, and it also provides the policy basis for the government and relevant departments to make employment decisions.

Analysis of College Art Teaching System under the Background of Video Big Data Technology

Art teaching needs not only learning art knowledge but also a lot of practice and aesthetic appreciation. However, traditional teaching methods cannot provide students with a large number of relevant learning materials, which is not conducive to improving students’ classroom enthusiasm. This paper presents the design and implementation of college art teaching system based on video big data technology and combines video recommendation algorithm with the Django teaching video website. The system analyzes the preference needs according to the behavior data of students watching videos and recommends videos for students. At the same time, the system can also evaluate the quality of the video content according to the behavior of students watching videos, reverse classify the video, and then optimize the recommendation results. The video recommendation algorithm model based on user behavior is better than the traditional collaborative filtering recommendation algorithm and fully connected neural network collaborative filtering algorithm. It can reduce the range of users who need similarity calculation and improve the accuracy of recommendation algorithm. The experimental results show that the fully connected neural network collaborative filtering algorithm has good recommendation performance and stability, can reduce the computational complexity, and can improve the recommendation accuracy. The teaching technology integrated through the Internet can greatly improve students’ enthusiasm for art teaching.

Research on social recommendation algorithm based on fuzzy subjective trust

In order to resist malicious attacks and improve recommendation accuracy, this paper proposes a collaborative filtering recommendation algorithm based on fuzzy subjective trust based on the subjectivity and fuzziness of trust. Firstly, the trust matrix is constructed by calculating the trust degree between users based on fuzzy subjective trust model and set of trusted users. Then, based on the traditional collaborative filtering recommendation algorithm, the user hybrid recommendation matrix is obtained through user similarity matrix and trust matrix to form recommendations for target users. Experimental results show that this method can effectively improve the resistance to malicious attacks and improve the accuracy of the recommendation system.

Collaborative Filtering Algorithm-Based Destination Recommendation and Marketing Model for Tourism Scenic Spots.

The information age of rapid development of tourism industry provides abundant travel information, but it also comes with the problem of information overload, which makes it difficult to meet the growing personalized needs of people. The traditional collaborative filtering recommendation algorithm (CFA) also suffers from the problem of data sparsity when the user population increases. Therefore, this study optimizes the CFA through the similarity factor and correlation factor and enhances the tourism sense of travel experience through the satisfaction balance strategy. The experimental results show that the improved CFA method has the highest average accuracy on the overall dataset and the best recommendation performance of the satisfaction balance strategy. Overall, the recommendation model in this study is useful for attraction selection of users and marketing optimization of travel companies.

Collaborative Filtering Recommendation Algorithm Based on User Attributes and Item Score

To solve the problems of cold start and data sparseness existing in traditional collaborative filtering recommendation algorithm, a collaborative filtering recommendation algorithm based on user attributes and item scoring is proposed. Firstly, we improve the credibility of user similarity and explore the potential interests of users, a new user rating similarity calculation method is constructed by introducing confidence, item popularity, and Pearson weighting. Secondly, we construct a user attribute similarity measurement method by introducing cultural distance, age attribute similarity, and user label similarity. Finally, user rating similarity and user attribute similarity are weighted to form a new similarity measurement model. Through simulation comparison between the collaborative filtering recommendation algorithm and the traditional recommendation algorithm, our results show that the collaborative filtering recommendation algorithm can effectively improve the accuracy of recommendations and the diversity of results and effectively alleviate the problem of data sparseness.

Three-way Naive Bayesian collaborative filtering recommendation model for smart city

Smart city construction has penetrated all aspects of human life. People can continue to feel the convenience of life brought by new technologies and new services. In order to further optimize the smart city system, aiming at the sparsity of scoring data and the difficulty of two-way decisions to deal with uncertain decisions in the recommendation algorithm, a three-way Naive Bayesian Collaborative Filtering Recommendation model (3NBCFR) for smart city is constructed by integrating Naive Bayesian, three-way decisions and collaborative filtering algorithm. Firstly, we consider the influence of item attributes on user scoring. Naive Bayesian classifier is used to score unrated items, which can effectively fill in the missing values in the score matrix and solve the problem of data sparsity. Secondly, to ensure sustainable recommendations for uncertain goals, the three-way decisions is introduced into the collaborative filtering recommendation system, three-way recommendation rules are formulated, and 3NBCFR is constructed. Finally, the model is applied to the movie recommendation system. The experiment on Movielens shows that compared with the traditional collaborative filtering recommendation algorithm, 3NBCFR algorithm reduces the recommendation cost and improves the recommendation quality. At the same time, it lays a foundation for promoting the construction of smart city.

5G Multimedia Precision Marketing Based on the Improved Multisensor Node Collaborative Filtering Recommendation Algorithm

The rapid development of web technology has brought new problems and challenges to the recommendation system: on the one hand, the traditional collaborative filtering recommendation algorithm has been difficult to meet the personalized recommendation needs of users; on the other hand, the massive data brought by web technology provides more useful information for recommendation algorithms. How to extract features from this information, alleviate sparsity and dynamic timeliness, and effectively improve recommendation quality is a hot issue in the research of recommendation system algorithms. In view of the lack of an effective multisource information fusion mechanism in the existing research, an improved 5G multimedia precision marketing based on an improved multisensor node collaborative filtering recommendation algorithm is proposed. By expanding the input vector field, the features of users’ social relations and comment information are extracted and fused, and the problem of collaborative modelling of these two kinds of important auxiliary information is solved. The objective function is improved, the social regularization term and the internal regularization term in the vector domain are analysed and added from the perspective of practical significance and vector structure, which alleviates the overfitting problem. Experiments on a large number of real datasets show that the proposed method has higher recommendation quality than the classical and mainstream baseline algorithm.

Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction

Current data has the characteristics of complexity and low information density, which can be called the information sparse data. However, a large amount of data makes it difficult to analyse sparse data with traditional collaborative filtering recommendation algorithms, which may lead to low accuracy. Meanwhile, the complexity of data means that the recommended environment is affected by multiple dimensional factors. In order to solve these problems efficiently, our paper proposes a multidimensional collaborative filtering algorithm based on improved item rating prediction. The algorithm considers a variety of factors that affect user ratings; then, it uses the penalty to account for users’ popularity to calculate the degree of similarity between users and cross-iterative bi-clustering for the user scoring matrix to take into account changes in user’s preferences and improves on the traditional item rating prediction algorithm, which considers user ratings according to multidimensional factors. In this algorithm, the introduction of systematic error factors based on statistical learning improves the accuracy of rating prediction, and the multidimensional method can solve data sparsity problems, enabling the strongest relevant dimension influencing factors with association rules to be found. The experiment results show that the proposed algorithm has the advantages of smaller recommendation error and higher recommendation accuracy.

Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

The recommendation algorithm is a very important and challenging issue for a personal recommender system. The collaborative filtering recommendation algorithm is one of the most popular and effective recommendation algorithms. However, the traditional collaborative filtering recommendation algorithm does not fully consider the impact of popular items and user characteristics on the recommendation results. To solve these problems, an improved collaborative filtering algorithm is proposed, which is based on the Term Frequency-Inverse Document Frequency (TF-IDF) method and user characteristics. In the proposed algorithm, an improved TF-IDF method is used to calculate the user similarity on the basis of rating data first. Secondly, the multi-dimensional characteristics information of users is used to calculate the user similarity by a fuzzy membership method. Then, the above two user similarities are fused based on an adaptive weighted algorithm. Finally, some experiments are conducted on the movie public data set, and the experimental results show that the proposed method has better performance than that of the state of the art.

Collaborative Filtering Recommendation Algorithm Based on Class Correlation Distance

Background: A class correlation distance collaborative filtering recommendation algorithm is proposed to solve the problems of category judgment and distance metric in the traditional collaborative filtering recommendation algorithm, which is using the advantage of the distance between the same samples and the class related distance. First, the class correlation distance between the training samples is calculated and stored. Second, the K nearest neighbor samples are selected, the class correlation distance of training samples and the difference ratio between the test samples and training samples are calculated respectively. Finally, according to the difference ratio, we classify the different types of samples. The experimental result shows that the algorithm combined with user rating preference can get lower MAE value, and the recommendation effect is better. With the change of K value, CCDKNN algorithm is obviously better than KNN algorithm and DWKNN algorithm, and the accuracy performance is more stable. The algorithm improves the accuracy of similarity and predictability, which has better performance than the traditional algorithm. Objective: A class correlation distance collaborative filtering recommendation algorithm is proposed to solve the problems of category judgment and distance metric in the traditional collaborative filtering recommendation algorithm, which is using the advantage of the distance between the same samples and the class related distance. Methods: First, the class correlation distance between the training samples is calculated and stored. Second, the K nearest neighbor samples are selected, the class correlation distance of training samples and the difference ratio between the test samples and training samples are calculated respectively. Finally, according to the difference ratio, we classify the different types of samples. Results: The experimental result shows that the algorithm combined with user rating preference can get lower MAE value, and the recommendation effect is better. Conclusion: With the change of K value, CCDKNN algorithm is obviously better than KNN algorithm and DWKNN algorithm, and the accuracy performance is more stable. The algorithm improves the accuracy of similarity and predictability, which has better performance than the traditional algorithm.

Personalized recommendation algorithm based on online comment sentiment analysis

Artificial intelligence is one of the many branches of computer science. With the continuous development of information technology and computer networks, artificial intelligence technology has also received tremendous support and rapid development. In the computer field, recommendation systems can be said to be the most extensive and successful application of artificial intelligence technology. This paper focuses on the problems of cold start and low recommendation accuracy caused by sparse rating data in traditional recommendation systems, proposes a personalized recommendation algorithm based on online comment sentiment analysis. The algorithm uses feature-level sentiment analysis to mine user preference information implicit in comments, and finally implements recommendations based on user ratings and preference information. Experiments show that the algorithm proposed in this paper can effectively make up for the shortcomings of traditional collaborative filtering recommendation algorithms, and is of great help in improving user cold start and the quality of recommendation results.

Design of Hybrid Recommendation Algorithm Based on User Dynamic Behavior and Static Attributes

Collaborative filtering recommendation algorithm is one of the most successful recommendation algorithms, but the traditional collaborative filtering recommendation algorithm is proved to have a series of problems in data sparsity, cold boot and scalability. Based on the problems mentioned above, this paper analyses user dynamic behavior and a hybrid recommendation algorithm based on user dynamic behavior and static attributes (UDBSA) was proposed in this paper. By determining the optimal value of BP critical point for the number of ratings, the recommendation strategy was dynamically selected according to the number of ratings. This method can comprehensively alleviate the influence of problems of new users and concept drift related problem on recommendation results.

Application of Travel Service Recommendation Algorithm Based on Cloud Computing

In the context of big data, traditional collaborative filtering recommendation algorithms cannot provide users with accurate recommendation services, making the sparsity of user data become an important factor affecting the accuracy of recommendation in the complex social network environment. This paper mainly studies the design and application of travel service recommendation algorithm based on cloud computing. In this paper, the data is processed based on MapReduce parallel computing framework to improve the performance and speed of the algorithm. The Linux-equipped cluster is deployed under the Hadoop framework. The cluster feasibility test is carried out to realize the recommendation function of the algorithm, and the function of the algorithm is realized according to the actual problems. On the Hadoop big data platform, the traditional algorithm was improved and the hadoop-based collaborative filtering recommendation algorithm was implemented to further improve the recommendation rate and accuracy of the traditional recommendation filtering algorithm.

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

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.

A novel recommendation algorithm with knowledge graph

Personalized recommendation is an important topic in recommendation algorithm research. Traditional collaborative filtering recommendation algorithms suffer the sparseness and cold start problems. Existing knowledge graph based recommendation algorithms miss the high-order similarity at the subgraph level. This paper introduces a RNN-based distributed representation model of knowledge graph called KG-GRU, which uses a sequence containing nodes and relationships to model the subgraph similarity in the same embedded vector space. Then, a personalized recommendation algorithm based on knowledge graph called KG-GRU4Rec, is proposed. KG-GRU4Rec implements an end-to-end predictive user rating model. Experimental results demonstrate that KG-GRU learns more accurate knowledge about entities and relationships in the graph and KG-GRU4Rec outperforms the comparison algorithm in both hit ratio and average reciprocal ranking.

A movie recommendation model combining time information and probability matrix factorisation

A deep analysis and discussion of matrix factorisation technologies are given in this paper taking into account the defects of traditional collaborative filtering recommendation algorithms. In addition, we provide an analysis of the effects of feature vector dimensions on the recommendation quality and efficiency of a probability matrix factorisation (PMF) algorithm. A PMF algorithm will lead to inaccurate recommendations if it does not consider possible dynamic changes in a user's interest over time. Accordingly, a TPMF model, a PMF algorithm integrated with time information, is proposed in this article. Its feasibility and effectiveness are empirically verified using movie recommendation datasets, and higher prediction accuracy is confirmed compared to existing recommendation algorithms.