Collaborative Filtering Recommendation Algorithm Based on Hybrid Similarity

Recommended system is beneficial to e-commerce sites, which provides customers with product information and recommendations; the recommendation system is currently widely used in many fields. In an era of information explosion, the key challenges of the recommender system is to obtain valid information from the tremendous amount of information and produce high quality recommendations. However, when facing the large mount of information, the traditional collaborative filtering algorithm usually obtains a high degree of sparseness, which ultimately lead to low accuracy recommendations. To tackle this issue, we propose a novel algorithm named Collaborative Filtering Recommendation Based on Trust Model with Fused Similar Factor, which is based on the trust model and is combined with the user similarity. The novel algorithm takes into account the degree of interest overlap between the two users and results in a superior performance to the recommendation based on Trust Model in criteria of Precision, Recall, Diversity and Coverage. Additionally, the proposed model can effectively improve the efficiency of collaborative filtering algorithm and achieve high performance.

A key challenge of the collaborative filtering (CF) information filtering is how to obtain the reliable and accurate results with the help of peers' recommendation. Since the similarities from small-degree users to large-degree users would be larger than the ones in opposite direction, the large-degree users' selections are recommended extensively by the traditional second-order CF algorithms. By considering the users' similarity direction and the second-order correlations to depress the influence of mainstream preferences, we present the directed second-order CF (HDCF) algorithm specifically to address the challenge of accuracy and diversity of the CF algorithm. The numerical results for two benchmark data sets, MovieLens and Netflix, show that the accuracy of the new algorithm outperforms the state-of-the-art CF algorithms. Comparing with the CF algorithm based on random walks proposed by Liu et al. (Int. J. Mod. Phys. C, 20 (2009) 285) the average ranking score could reach 0.0767 and 0.0402, which is enhanced by 27.3% and 19.1% for MovieLens and Netflix, respectively. In addition, the diversity, precision and recall are also enhanced greatly. Without relying on any context-specific information, tuning the similarity direction of CF algorithms could obtain accurate and diverse recommendations. This work suggests that the user similarity direction is an important factor to improve the personalized recommendation performance.

Recommender system is one of the most important technologies in electronic commerce. And the collaborative filtering is almost the popular approach used in the recommender systems. With the development of electronic commerce systems, the magnitudes of users and items grow rapidly, resulted in the extreme sparsity of user rating data set. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. Sparsity of users’ ratings is the major reason causing the poor quality. To address this issue, a collaborative filtering recommendation algorithm based on singular value decomposition (SVD) smoothing is presented. This approach predicts item ratings that users have not rated by the employ of SVD technology, and then uses Pearson correlation similarity measurement to find the target users’ neighbors, lastly produces the recommendations. The collaborative filtering recommendation algorithm based on SVD smoothing can alleviate the sparsity problems of the user item rating dataset, and can provide better recommendation than traditional collaborative filtering algorithms.

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.

The most classic algorithm in the recommender system domain is the collaborative filtering algorithm, but it often suffers from data sparsity and item cold-start problems. Moreover, most traditional collaborative filtering algorithms only consider the user's rating of the item, which greatly limits the accuracy of the recommendation. This paper takes movie recommendation as the research object, and proposes a collaborative filtering recommendation model based on LDA topic model and network embedding, which improves the dependability of the model. The model first uses the LDA topic model to extract the text features of users and movies from texts, then builds the association network through the association features between users, and learns the network features using node2vec and GraphGAN. Finally, the two parts are combined to compute the similarity between users to recommend movies using the collaborative filtering algorithm. Results of the experiment on the real dataset of the Douban website demonstrate that movie recommendation with the user characteristics mined by the LDA model and GraphGAN combined can achieve the best effect, which is much higher than the collaborative filtering based on user ratings or using only LDA theme. This proves that network embedding can promote the accuracy of recommendationation, while ensuring the dependability of the model. The method proposed in this paper is effective.

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.

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.

Making recommendations from top-N user-item subgroups

In view of the sparsity of user-resource interaction data, information asymmetry caused by cold start, and weak personalized resource allocation in the current integration of enterprises with vocational schools and universities, this article adopts the Collaborative Filtering (CF) algorithm to mine the user’s behavior patterns and preferences, establish a more intelligent resource recommendation model, and enhance the relevance and accuracy of resource recommendation. The intelligent resource recommendation system plays a key role in the construction of smart cities, fully optimizing the interaction between educational institutions and enterprises, and providing strong support for promoting the development of smart cities. First, an embedded feature expression for users and resources is established, and the deep embedding learning method is used to extract the association between high-dimensional and nonlinear features. Secondly, based on the traditional CF algorithm, the neural network architecture is combined to achieve accurate matching between users and resources. Finally, through experiments in user cold start and resource cold start scenarios, the performance improvement of the recommendation system is verified and compared with the widely used Matrix Factorization (MF) and Neural Collaborative Filtering (NCF) for analysis. According to the experimental results, the average precision of the proposed method is 0.804, which is 7.34% and 5.51% higher than MF and NCF respectively. The conclusion shows that the CF algorithm helps to enhance the resource recommendation effect and provides a new perspective for enhancing service personalization.

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.

Recommendation system has been applied in many fields, such as Human Computer Interaction, Information Retrieval, and so on. Essentially speaking, these are based on data mining. Among the many algorithms used in the recommendation system, collaborative filtering algorithm is one of the most mature and most widely used algorithm. With the coming of big data era, a variety of recommendation algorithm surge, and distributed recommendation algorithm has become a hot research topic. As a sub project of Hadoop , Mahout is also very obvious to support collaborative filtering algorithm. In order to deeply analyze the operation principle and the use effect of Collaborative Filtering Recommendation Algorithm in mahout, we designed a combination model composed of the recommender and the similarity measure. Using the custom data set, we write some Java classes to test collaborative filtering algorithm based on user and the collaborative filtering algorithm based on the item, the experimental results shows that our combination model get a better classification effect .

To overcome several limitations in the research area of collaborative filtering(CF),this paper presents a CF recommendation algorithm,named UNCF(Uncertain Neighbors' Collaborative Filtering Recommendation Algorithm).In the reality,the scene of recommendation is uncertain.The similarities computations of both user-based and item-based are considered to choose the neighbors dynamically as the recommendation set.This set can be used to select the trustworthy subset which is the most effective objects to the predicted result.Moreover,this paper defines a new prediction algorithm that combines the advantages of trustworthy subset for this uncertain recommendation method.Through experimental results,the UNCF algorithm can consistently achieve better prediction accuracy than traditional CF algorithms,and effectively leverage the result in the uncertain environment.Furthermore,the algorithm can alleviate the dataset sparsity problem.

Collaborative filtering recommendation algorithm is the most important recommended application methods at present. The selection of users (items) similarity measurement methods in this method may affect the final recommendation effect. Probability matrix factorization algorithm is one of the more and more methods applied in collaborative filtering algorithms. This paper proposes to convert the users (items) consumption network into a bipartite graph and use belief propagation algorithm to obtain fuzzy nearest neighbor set, improving users (items) similarity measure and incorporating it into probability matrix factorization. Experimental tests on the data set showed that the proposed method performance was good.

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.

Collaborative filtering method is based on users with similar interests to provide recommendations. At present most collaborative filtering algorithms consider the similarity among users or the similarity among items without the consideration of the similarity between users and items. In this paper, biclustering algorithms based on Bimax and Order-Preserving Submatrix are introduced to measure the similarity from both the users and the items, compared with the traditional collaborative filtering algorithm proposed by Paul Resnick (1994), the Root mean square error (RMSE) and the Mean absolute error (MAE) of the former are lower than the latter's. The experiments implemented on the datasets Movielens (100k) and Movielens (1M) proves that the collaborative filtering algorithm based on OPSM has a comparative advantage in the recommendation system. Meanwhile the larger the dataset is, the more accurate the prediction will be.