Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering

The traditional collaborative filtering recommendation algorithm can be divided into the user-based and the item-based two methods, which only uses the information in the rating matrix. Because of the limitation of the information capacity they used, it is difficult to further improve the accuracy of the recommendation, and cold start problem also affects the normal operation of the recommendation system. This paper presented a collaborative filtering recommendation algorithm (UISA) fusing user-based, item-based and social networks data. The algorithm uses the data of the neighbor relations in social networks, calculating the users' friends not reflected in the rating matrix. At the same time, we can calculate the similarity between items by using the data of item text in social networks, mining similar items not reflected in the rating matrix. In this way, it can fundamentally expand available information capacity of the traditional filtering collaboration recommendation algorithms, improve the recommendation accuracy, alleviate cold start problem. Experimental results based on KDD CUP 2012 real data show that compared with the traditional collaborative filtering system, this system has obvious advantages in the recommendation accuracy and ease of cold start.

Everyday there are masses of information generated and the existence of a large amount of information makes it hardly to mining the wanted information. Personalized recommendation is the process to alleviative the problem. Collaborative filtering is one of the most popular technologies in the personal recommendation system. As the user rating matrix becoming extremely sparsity, traditional collaborative filtering recommendation algorithm calculates similarity between items using the rating data, and it does not consider the semantic relationship between different items, thus recommendation quality is very poor. To solve this problem, the paper combines the item semantic similarity and the item rating similarity, which takes into account the influence of item semantic and user rating to enhance the item-based collaborative filtering. The personalized collaborative filtering recommendation algorithm combining the item semantic similarity and item rating similarity can mitigate the sparsity problem in the electronic commerce recommender systems.

Chinese traditional culture contains the wisdom and experience of the concept of governing the country, and contains the moral principles taught by the ancients. Carrying forward and developing the excellent traditional Chinese culture is of great significance for enhancing cultural self-confidence. The purpose of this paper is to study digital cultural communication based on recommendation algorithms. Combined with the particularity of the existence of digital culture, the recommendation technology is studied, aiming at the problems existing in collaborative filtering recommendation technology, an improvement method is proposed, and a recommendation algorithm for digital cultural resources is proposed. Combined with the digital cultural resource recommendation algorithm, a digital cultural resource recommendation engine is designed and implemented. Through the realization of the algorithm, using the data in the “Digital Cultural Resource Database” of the scientific research achievement of the Key Laboratory of Cultural Informatization, the digital cultural resource recommendation algorithm is compared with the traditional collaborative filtering recommendation algorithm, and the MAE mean absolute deviation is used as the main According to the experimental data, the MAE values of the improved collaborative filtering digital culture recommendation algorithm are all lower than 0.15 points, and the conclusion is drawn that the improved recommendation algorithm has higher recommendation quality.

With the arrival of the era of big data, recommendation algorithm has become a hot research field in internet life and social business. However, the traditional recommendation algorithms have problems of cold start, scoring matrix data sparse, affecting the accuracy of recommendation. This paper proposes a hybrid recommendation algorithm based on the SVD (singular value decomposition) and Slope One algorithm, experiments results show that the algorithm proposed by this paper has superior performance compared with the traditional collaborative filtering recommendation algorithm. Keywords-SVD; Slope-One; Recommendation Algorithm; Collaborative Filtering

At present, the Internet products for the study of user interests and hobbies data mining and then recommended to increase user retention and user stickiness behaviour has been a common practice, music hobbies as an important point of interest for thousands of users, and its recommendation has become an important content of social life and has been the subject of more and more attention by the researchers, based on the collaborative filtering algorithm of the music recommendation is the use of the particularly wide range of recommendation algorithms. Music recommendation based on collaborative filtering algorithm is one of the most widely used recommendation algorithms, which mainly analyses the similarity between users or items, and completes the recommendation process based on this, but it has the problems of poor recommendation diversity and low coverage. Therefore, this paper designs comparative experiments to compare the recommendation algorithms based on FP-growth and collaborative filtering, relying on Last.fm's real user data warehouse, and demonstrates through experimental comparative research that compared with the collaborative filtering recommendation algorithm, the results of the recommendation content based on the FP-growth algorithm have a certain degree of enhancement in terms of diversity and coverage, and can produce a certain degree of accuracy in terms of the recommendation of music. accuracy, it can produce certain reference value.

With the development of e-commerce, people have more pursuit for online shopping. In the online shopping environment, users also need a good experience when browsing goods online. So now more and more people choose to buy their favorite agricultural products with certain characteristics, affordable and reassuring through the Internet. Therefore, the research on agricultural product recommendation model and e-commerce system design based on collaborative filtering recommendation (CFR) algorithm is very useful. Firstly, this paper introduces the characteristics of agricultural product recommendation, then studies the application of collaborative filtering algorithm, designs and develops the agricultural product recommendation model and e-commerce system based on the algorithm, and tests the function of the model. Finally, the test results show that the short-term prediction accuracy of the system is slightly low, but the accuracy is relatively high, covering a wide range of agricultural products, and the user coverage is also high, which can meet the shopping needs of users for agricultural products.

With the speedy development of network, information technology has provided an unmatched amount of information resources. It has also led to the problem of information overload. However, people experiences and knowledge often do not enough to process the vast amount of usable information. Thus, approaches to help find resources of interest have attracted much attention from researchers. And recommender systems have arrived to solve this problem. Recommender system plays an important role mainly in an electronic commerce environment as a new marketing strategy. Although a varied of recommendation techniques has been developed recently, collaborative filtering has been known to be the most successful recommendation techniques and has been used in a number of different applications. But traditional collaborative filtering recommendation algorithm has the problem of sparsity. Aiming at the problem of data sparsity for personalized filtering systems, a collaborative filtering recommendation algorithm based on user rating similarity and user attribute similarity is given. This approach not only considers the user item rating information, but also takes into account the user attribute.

In this paper, we focus on how to overcome several limitations in the traditional research of collaborative filtering(CF). We present a novel CF recommendation algorithm, named DSNP(Dynamic Similar Neighbor Probability). This algorithm improves the neighbors’ similarities computations of both users and items to choose the neighbors dynamically as the recommendation sets. How to select the confident subsets which are the most effective neighbors to the target object, it is the first stage. A major innovation is by defining a dynamic neighbor probability over the trustworthy subsets. Moreover, we define a prediction algorithm that combines the advantages of dynamic neighbor coefficient with the user-based CF and the item-based CF algorithms. Experimental results show that the algorithm can achieve consistently better prediction accuracy than traditional CF algorithms, and effectively leverage the result between user-based CF and item-based CF. Furthermore, the algorithm can alleviate the dataset sparsity problem.

Aiming at the problem of lack of time information in traditional collaborative filtering recommendation algorithm, the dynamic characteristics of user preference drift over time are analyzed. An improved scheme of collaborative filtering algorithm based on time characteristic is proposed. In calculating the predictive score, taking into account the characteristics of user interest decay over time, the time decay function is introduced as the weight of the loss function, the lower the weight for the interaction farther from the present time, the closer the interaction is with the higher weight, Element-wise Alternating Least Squares (eALS) is used to improve model training speed when updating models. Using the public dataset Yelp to conduct experiments, compared with the traditional RCD method, the predicted hit rate using the eALS method is significantly improved, and the T-eALS method with the added time information makes the hit rate increase significantly, and the experimental results show that this method can effectively integrate time information and provide users with more accurate recommendations.

CF (Collaborative filtering) algorithm has the widest and most successful applications in personalized recommendations. However, due to its over-reliance on the users' historical data, it is difficult to avoid data sparseness and cold start issues. The data sparseness and cold start may cause poor recommendation accuracy of the collaborative filtering algorithm. A hybrid optimal collaborative filtering algorithm based on users' characteristics and trust is proposed in this paper. In the process of users' similarity calculation, the age and gender of users' characteristics are introduced to make the determination of nearest neighbor more accurate. Besides, in order to improve the recommendation accuracy of the traditional CF recommendation algorithm, the trust relationship is introduced into the prediction score by measuring the users' trust, and this improvement will be used in the recommendation of new items in order to improve the recommendation accuracy of the traditional CF recommendation algorithm. The experimental results of Movie lens data set show that the improved recommendation accuracy of the recommendation system can be achieved by the proposed algorithm. Also, the problems of cold start and sparse data can be solved effectively.

Traditional collaborative filtering algorithms use user history rating information to predict movie ratings Other information, such as plot and director, which could provide potential connections are not fully mined. To address this issue, a collaborative filtering recommendation algorithm named a movie recommendation method based on knowledge graph and time series is proposed, in which the knowledge graph and time series features are effectively integrated. Firstly, the knowledge graph gains a deep relationship between users and movies. Secondly, the time series could extract user features and then calculates user similarity. Finally, collaborative filtering of ratings can calculate the user similarity and predicts ratings more precisely by utilizing the first two phases’ outcomes. The experiment results show that the A Movie Recommendation Method Fusing Knowledge Graph and Time Series can reduce the MAE and RMSE of user-based collaborative filtering and Item-based collaborative filtering by 0.06,0.1 and 0.07,0.09 respectively, and also enhance the interpretability of the model.

Aiming at the problem that the traditional Collaborative Filtering algorithm has low recommendation accuracy, in the paper, we propose a collaborative filtering recommendation algorithm based on the global trust degree integrating the direct trust information in the social networks.We first transform the local trust relationships to the global trust relationship by the rules in the trust network, and get the trust rank of all users in the trust networks; Then we use the global trust value to instead of the similarity information value as the weights of a predicted formula in the traditional collaborative recommendation algorithm, and integrate the weights to the matrix factorization-based recommendation model.

Currently collaborative filtering is widely used in e-commerce, digital libraries and other areas of personalized recommendation service system. Nearest-neighbor algorithm is the earliest proposed and the main collaborative filtering recommendation algorithm, but the data sparsity and cold-start problems seriously affect the recommendation quality. To solve these problems, A collaborative filtering recommendation algorithm based on users' social relationships is proposed. 0n the basis of traditional filtering recommendation technology, it combines with the interested objects of user's social relationship and takes the advantage of the tags to projects marked by users and their interested objects to improve the methods of recommendation. The experimental results of MAE ((Mean Absolute Error)) verify that this method can get better quality of recommendation.

Personalized recommendation system is an important means for people to get interested information and product quickly. This traditional user-based collaborative filtering algorithm cost too much computation on similarity calculation. In order to solve this problem, a new collaborative filtering recommendation algorithm based on K-Means clustering of user’s attributes is proposed. In this algorithm, the longitude and latitude of users’ are first clustered, and then the similarity of users’ are calculated within each cluster. Finally, parallelization of this proposed algorithm on Spark is implemented. Experiments show that the user attributes-based collaborative filtering has satisfied performance.

Traditional collaborative filtering recommendation algorithm has some problems, such as sparse data and cold start, and the purchase of insurance products is easily affected by the characteristics of the insured. Therefore, a hybrid recommendation algorithm based on time weighted optimization is proposed, which combines knowledge graph and collaborative filtering recommendation. Knowledge graph extracts the attributes of users and objects as features, constructs triples for training, and shares potential features by cross compression units, which can fully excavate the potential relationship between different information, and can also extend the user preferences by using multiple relationships to ensure the diversity of recommendations; the timeliness of recommendation is guaranteed by time weighting, and the timeliness of recommendation is further considered The influence of age on the purchase preference of the insured better adapts to the characteristics of insurance products. Through the empirical analysis on the insurance product data set, the experimental data show that the algorithm is better in each evaluation index compared with the existing algorithm.