Product recommendation algorithm based on users’ reviews mining

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

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.

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.

Aiming at the fact that the traditional collaborative filtering recommendation algorithm is insufficient in the number of users’ implicit feedback, and the user interest preference model is too rough, a collaborative filtering recommendation algorithm with the importance of tags is proposed. Type and frequency of use of the label reflect user preferences and preferences, in order to establish a new user preferences model for better mining and use implicit user feedback data will affect the degree of the label on the user to quantify, to establish a new method for similarity computation. The experimental results show that the proposed algorithm has obvious advantages, improves the recommendation accuracy and alleviates the cold start problem.

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.

Traditional collaborative filtering recommendation algorithms face the cold-start problem. A collaborative filtering recommendation algorithm based on the implicit information of the new users and multi-attribute rating matrix is proposed to solve the problem. The implicit information of the new users is collected as the first-hand interest information. It is combined with other rating information to create a user-item rating matrix (UIRM). Singular value decomposition is used to reduce the dimensionality of the UIRM, resulting in the initial neighbor set for target users and a new user-item rating matrix. The user ratings are mapped to the relevant item attributes and the user attributes respectively to generate a user-item attribute rating matrix and a user attribute-item attribute rating matrix (UAIARM). The attributes of new items and UAIARM are matched to find the N users with the highest match degrees as the target of the new items. The attributes of the new users are matched with UAIARM to find the N items with the highest match degrees as the recommended items. Experiment results validate the feasibility of the algorithm.

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.

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.

Personalized recommendation of film and television culture is an important content to meet people’s daily cultural needs and social information. Promoting the personalized recommendation of film and television culture is conducive to promoting the more efficient use of network resources. However, in recent years, the film and television culture industry has developed rapidly, and the production of film and television culture has also increased year by year. How to quickly and accurately find the user’s favorite film and television culture in the massive film and television cultural data has become an urgent problem to be solved. Aiming at the shortcomings of the film and television culture recommendation system, this paper proposes a new personalized recommendation algorithm for film and television culture based on an intelligent classification algorithm. Based on the preliminary screening results of the traditional collaborative filtering recommendation algorithm, the user data and video data are used as input and the video score as output, which is further filtered by a convolutional neural network. Finally, selecting the film and television culture recommendation set that is most suitable for the current user can also make up for the cold start problem of collaborative filtering at the beginning of the system operation. The simulation experiment is carried out. The experimental results show that the personalized recommendation algorithm based on an intelligent classification algorithm improves the scoring accuracy by 0.15, which indicates that the designed film and television culture recommendation system has a good application effect.

In view of a series of problems in college book recommendation, such as cold start of users, high proportion of popular recommendations and low recommendation accuracy, this paper improves these problems based on the problems of the existing collaborative filtering algorithm and combined with the characteristics of college book borrowing. An improved University book recommendation algorithm is proposed, in which the time attenuation factor is added when generating the user evaluation matrix, and the influence of gender and professional factors on the user feature similarity is considered. The algorithm solves the problem of insufficient score of collaborative filtering algorithm. Experiments show that this algorithm is better than the traditional collaborative filtering recommendation algorithm and can meet the actual needs.

In order to reflect the actual case of users' decisions and rating patterns, and solve the sparsity problem of traditional collaborative filtering recommendation algorithms, a trapezoid fuzzy rating model is proposed, which fuzzifies crisp point into trapezoid fuzzy number based on the rating stat istical information of users. The trapezoid fuzzy number contains personalized information of users and can reasonably represent user's preference degree and rating pattern. Based on this model, the user fuzzy similarity-based collaborative filtering recommendation algorithm is designed. We prove that this algorithm is a fuzzy extension of Cosine similarity, and analyze the application scopes of this method. The experimental results show that our method can obtain better performance than other traditional methods, especially when implemented in a sparse dataset with high ratio between users and items.

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.

Traditional collaborative filtering algorithm does not take into account the user’s interest factors, at the same time, it has the problem of sparse data, poor scalability and so on, which directly affects the quality of the recommendation. This paper proposes a collaborative filtering recommendation algorithm based on user interest clustering. Firstly, according to the user’s existing score, a method is used to compute the interest degree of the user. Secondly, a clustering algorithm is used to divide the users into clusters. Finally, a collaborative filtering algorithm is used to recommend movie, it effectively improves data sparsity and real-time of problem. The experimental results exhibit that the proposed algorithm shows a great improvement in the recommendation accuracy.

At present, the traditional collaborative filtering (CF) recommendation algorithm generally has the problems of data sparsity and low accuracy. In order to solving these problems, this paper proposes a multi-feature fusion collaborative filtering recommendation algorithm. Firstly, considering that different users have different preferences for different item attributes, this paper used item attribute scores and user preference information for matrix filling to alleviate the problem of data sparsity. Secondly, considering the influence of multiple features of user activity , the count of common item ratings, and item popularity on the calculation of user similarity, and used these features to improve the calculation of similarity. Finally, the matrix after data filling and the improved similarity calculation formula were used to make personalized recommendations for users. Experimental results prove that compared with traditional algorithms and other improved algorithms, the improved algorithm in this paper alleviates the problem of data sparsity to a certain extent, and at the same time has a certain improvement in accuracy