Degree correlation effect of bipartite network on personalized recommendation

In this paper, by introducing a new user similarity index base on the diffusion process, we propose a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the proposed algorithm, the degree correlation between users and objects is taken into account and embedded into the similarity index by a tunable parameter. The numerical simulation on a benchmark data set shows that the algorithmic accuracy of the MCF, measured by the average ranking score, is further improved by 18.19% in the optimal case. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that the presented algorithm can provide more diverse and less popular recommendations, for example, when the recommendation list contains 10 objects, the diversity, measured by the hamming distance, is improved by 21.90%.Comment: 9 pages, 3 figure

Improved collaborative filtering algorithm via information transformation

In this paper, we propose a spreading activation approach for collaborative filtering (SA-CF). By using the opinion spreading process, the similarity between any users can be obtained. The algorithm has remarkably higher accuracy than the standard collaborative filtering using the Pearson correlation. Furthermore, we introduce a free parameter β to regulate the contributions of objects to user–user correlations. The numerical results indicate that decreasing the influence of popular objects can further improve the algorithmic accuracy and personality. We argue that a better algorithm should simultaneously require less computation and generate higher accuracy. Accordingly, we further propose an algorithm involving only the top-N similar neighbors for each target user, which has both less computational complexity and higher algorithmic accuracy

An improved switching hybrid recommender system using naive Bayes classifier and collaborative filtering

Recommender Systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. To date a number of recommendation algorithms have been proposed, where collaborative filtering and content-based filtering are the two most famous and adopted recommendation techniques. Collaborative filtering recommender systems recommend items by identifying other users with similar taste and use their opinions for recommendation; whereas content-based recommender systems recommend items based on the content information of the items. These systems suffer from scalability, data sparsity, over specialization, and cold-start problems resulting in poor quality recommendations and reduced coverage. Hybrid recommender systems combine individual systems to avoid certain aforementioned limitations of these systems. In this paper, we proposed a unique switching hybrid recommendation approach by combining a Naive Bayes classification approach with the collaborative filtering. Experimental results on two different data sets, show that the proposed algorithm is scalable and provide better performance – in terms of accuracy and coverage – than other algorithms while at the same time eliminates some recorded problems with the recommender systems

Improved collaborative filtering algorithm based on heat conduction

In this paper, we present an improved collaborative filtering (ICF) algorithm by using the heat diffusion process to generate the user correlation. This algorithm has remarkably higher accuracy than the standard collaborative filtering (CF) using Pearson correlation. Furthermore, we introduce a free parameter β to regulate the contributions of objects to user correlation. The numerical simulation results indicate that decreasing the influence of popular objects can further improve the algorithmic accuracy and diversit

A preprocessing method for improving effectiveness of Collaborative Filtering

Collaborative filtering uses information about customers’ preferences to make personal product recommendations and is achieving widespread success in e-Commerce. However, the traditional collaborative filtering algorithms do not response accurately to customers’ needs. The quality of the recommendation needs to be improved in order to support personalized service to each customer. In this paper, we present novel method to improve the accuracy of the collaborative filtering algorithm. We borrow vector space model from information retrieval theory and use it to effectively discriminate the preference weights on the items for each customer. The proposed method achieves more accurate recommendations for customers who purchase similar types of products repeatedly. Our experimental evaluation on the well-known MovieLens data set shows that our method does result in a better accuracy

iTrace: An Implicit Trust Inference Method for Trust-aware Collaborative Filtering

The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. A collaborative filtering (CF) algorithm recommends items of interest to the target user by leveraging the votes given by other similar users. In a standard CF framework, it is assumed that the credibility of every voting user is exactly the same with respect to the target user. This assumption is not satisfied and thus may lead to misleading recommendations in many practical applications. A natural countermeasure is to design a trust-aware CF (TaCF) algorithm, which can take account of the difference in the credibilities of the voting users when performing CF. To this end, this paper presents a trust inference approach, which can predict the implicit trust of the target user on every voting user from a sparse explicit trust matrix. Then an improved CF algorithm termed iTrace is proposed, which takes advantage of both the explicit and the predicted implicit trust to provide recommendations with the CF framework. An empirical evaluation on a public dataset demonstrates that the proposed algorithm provides a significant improvement in recommendation quality in terms of mean absolute error (MAE).Comment: 6 pages, 4 figures, 1 tabl

An Enhanced Neural Graph based Collaborative Filtering with Item Knowledge Graph

Recommendation system is a process of filtering information to retain buyers on e-commerce sites or applications. It is used on all e-commerce sites, social media platform and multimedia platform. This recommendation is based on their own experience or experience between users. In recent days, the graph-based filtering techniques are used for the recommendation to improve the suggestions and for easy analysing. Neural graph based collaborative filtering is also one of the techniques used for recommendation system. It is implemented on the benchmark datasets like Yelp, Gowalla and Amazon books. This technique can suggest better recommendations as compared to the existing graph based or convolutional based networks. However, it requires higher processing time for convolutional neural network for performing limited suggestions. Hence, in this paper, an improved neural graph collaborative filtering is proposed. Here, the content-based filtering is performed before the collaborative filtering process. Then, the embedding layer will process on both the recommendations to provide a higher order relation between the users and items. As the suggestion is based on hybrid recommendation, the processing time of Convolutional neural network is reduced by reducing the number of epochs. Due to this, the final recommendation is not affected by the smaller number of epochs and also able to reduce its computational time. The whole process is realized in Python 3.6 under windows 10 environment on benchmark datasets Go Walla and Amazon books. Based on the comparison of recall and NDCG metric, the proposed neural graph-based filtering outperforms the collaborative filtering based on graph convolution neural network

Ask the GRU: Multi-Task Learning for Deep Text Recommendations

In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an explicit mapping from text to factors. This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly-parametrized model. Previous work has used topic models or averages of word embeddings for this mapping. In this paper we present a method leveraging deep recurrent neural networks to encode the text sequence into a latent vector, specifically gated recurrent units (GRUs) trained end-to-end on the collaborative filtering task. For the task of scientific paper recommendation, this yields models with significantly higher accuracy. In cold-start scenarios, we beat the previous state-of-the-art, all of which ignore word order. Performance is further improved by multi-task learning, where the text encoder network is trained for a combination of content recommendation and item metadata prediction. This regularizes the collaborative filtering model, ameliorating the problem of sparsity of the observed rating matrix.Comment: 8 page

IMPROVING RECOMMENDATION PERFORMANCE WITH USER INTEREST EVOLUTION PATTERNS

Effective recommendation is indispensable to customized or personalized services. Collaborative filtering approach is a salient technique to support automated recommendations, which relies on the profiles of customers to make recommendations to a target customer based on the neighbors with similar preferences. However, traditional collaborative recommendation techniques only use static information of customers’ preferences and ignore the evolution of their purchasing behaviours which contain valuable information for making recommendations. Thus, this study proposes an approach to increase the effectiveness of personalized recommendations by mining the sequence patterns from the evolving preferences of a target customer over time. The experimental results have shown that the proposed technique has improved the recommendation precision in comparison with collaborative filtering method based on Top k recommendation

Knowledge-based recommendation with hierarchical collaborative embedding

© 2018, Springer International Publishing AG, part of Springer Nature. Data sparsity is a common issue in recommendation systems, particularly collaborative filtering. In real recommendation scenarios, user preferences are often quantitatively sparse because of the application nature. To address the issue, we proposed a knowledge graph-based semantic information enhancement mechanism to enrich the user preferences. Specifically, the proposed Hierarchical Collaborative Embedding (HCE) model leverages both network structure and text info embedded in knowledge bases to supplement traditional collaborative filtering. The HCE model jointly learns the latent representations from user preferences, linkages between items and knowledge base, as well as the semantic representations from knowledge base. Experiment results on GitHub dataset demonstrated that semantic information from knowledge base has been properly captured, resulting improved recommendation performance