Improving the Recommendation of Collaborative Filtering by Fusing Trust Network

Information recommender system is considered to be one of the most effective tools to solve the problem of information overload. Collaborative Filtering (CF), which utilizes similar neighbors to generate recommendations, is believed to be the most widely implemented and most mature technique for recommender systems. However, the recommendation results are often unsatisfactory due to the data sparsity of the input ratings matrix. Consequently, a hybrid recommender system which combines social network, trust network, and improved CF is proposed to enhance the accuracy of recommendation and overcome the weakness of data sparsity. Another advantage of the system is that utilizing the community structure discovered in social network as a new trust network sharply reduces the computation required for traditional CF. An empirical evaluation on Epinions.com dataset shows that the hybrid recommender system which incorporates social network and trust network into improved CF is more effective in terms of accuracy. This is especially evident on users who provided few ratings.

Aim: The coronavirus disease 2019 (COVID-19) pandemic caused the use of social networks in the field of information acquisition and transmission to increase, whereas the validity of the information available is questionable. Because people’s trust in these networks is important, this study aimed to utilize three data mining techniques to identify the hidden rules for detecting the user trust level of social networks in the context of COVID-19. Materials and Methods: An electronic questionnaire containing 27 questions was provided to users. Out of the 12 questions selected, the final question asked about the level of user trust in social networks and was considered the target class. Based on the range in value, question 12 was divided into five classes. The relevance of the remaining 11 questions was then assessed using three decision tree-based data mining techniques. Results: The results showed that the random forest technique performed better than the other techniques. Most social network users have a moderate level of trust in information regarding COVID-19; in fact, the medium class is the most widely used target class with 60% utilization rate, which affects sensitivity and specificity. The values of these measures were much higher for this class than for the other classes. Conclusion: The educational content, both its type and the amount, regarding COVID-19 that is provided on social networks, impact on user trust. As the existence of inconsistent information has had a negative impact on user trust, a small percentage of users have high trust in these networks.

In the Big Data Era, recommender systems perform a fundamental role in data management and information filtering. In this context, Collaborative Filtering (CF) persists as one of the most prominent strategies to effectively deal with large datasets and is capable of offering users interesting content in a recommendation fashion. Nevertheless, it is well‐known CF recommenders suffer from data sparsity, mainly in cold‐start scenarios, substantially reducing the quality of recommendations. In the vast literature about the aforementioned topic, there are numerous solutions, in which the state‐of‐the‐art contributions are, in some sense, conditioned or associated with traditional CF methods such as Matrix Factorization (MF), that is, they rely on linear optimization procedures to model users and items into low‐dimensional embeddings. To overcome the aforementioned challenges, there has been an increasing number of studies exploring deep learning techniques in the CF context for latent factor modelling. In this research, authors conduct a systematic review focusing on state‐of‐the‐art literature on deep learning techniques applied in collaborative filtering recommendation, and also featuring primary studies related to mitigating the cold start problem. Additionally, authors considered the diverse non‐linear modelling strategies to deal with rating data and side information, the combination of deep learning techniques with traditional CF‐based linear methods, and an overview of the most used public datasets and evaluation metrics concerning CF scenarios.

Assessing multi-hop interpersonal trust in online social networks (OSNs) is critical for many social network applications such as online marketing but challenging due to the difficulties of handling complex OSN topology, in existing models such as subjective logic, and the lack of effective validation methods. To address these challenges, we for the first time properly define trust propagation and combination in arbitrary OSN topologies by proposing 3VSL (Three-Valued Subjective Logic). The 3VSL distinguishes the posteriori and priori uncer- tainties existing in trust, and the difference between distorting and original opinions, thus be able to compute multi-hop trusts in arbitrary graphs. We theoretically proved the capability based on the Dirichlet distribution. Furthermore, an online survey system is implemented to collect interpersonal trust data and validate the correctness and accuracy of 3VSL in real world. Both experimental and numerical results show that 3VSL is accurate in computing interpersonal trust in OSNs. Index Terms—Interpersonal trust, trust establishment, online social networks, three-valued subjective logic, Dirichlet distribu- tion

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.

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.

Majority of the e-commerce sites implement Recommender Systems (RS) to help users navigate through the large search space and assist their decision making process by suggesting products that the user may like. Collaborative Filtering (CF) is the most successful and widely used algorithm in the domain of RS. However, due to the exponential growth of the web in terms of both content and number of users, CF based RS face serious scalability issues. To alleviate this problem, we propose a clustering based CF approach using two hierarchical space partitioning data structures — K-d tree and Quadtree. We cluster or partition the users’ space of the system on the basis of user location and then use the resultant clusters for predicting ratings of a target user. Since the CF based recommendation algorithm is applied separately to the clusters and not on the entire rating data, it helps in bringing down the runtime of the algorithm substantially. We further measure spatial autocorrelation indices in the clusters to justify our clustering method. However, our objective is not only to reduce the runtime but also to maintain an acceptable recommendation quality. This requirement is rightly addressed by the proposed method which assures scalability, by processing very large datasets using the same computing resource. Moreover our proposed clustering scheme is oblivious of the underlying CF algorithm. Results from the extensive experiments conducted, show that our hierarchical clustering based recommendation approach reduces runtime of the standard CF algorithms by about 88%, 82%, 79% and 85% for MovieLens-100K, MovieLens-1M, Book-Crossing and TripAdvisor data respectively, while maintaining good recommendation quality.

Studying recommendation method has long been a fundamental area in personalized marketing science. The rating data sparsity problem is the biggest challenge of recommendations. In addition, existing recommendation methods can only identify user preferences rather than customer needs. To solve these two bottleneck problems, we propose a novel implicit feedback recommendation method using user-generated content (UGC). We identify product feature and customer needs from UGC using Convolutional Neural Network (CNN) model and textual semantic analysis techniques, measure user-product fit degree introducing attention mechanism and antonym mechanism, and predict user rating based on user-product fit degree and user history rating data. Using data from a large-scale review sites, we demonstrate the effectiveness of our proposed method. Our study makes several research contributions. First, we propose a novel recommendation method with strong robustness against sparse rating data. Second, we propose a novel recommendation method based on the customer need-product feature fit. Third, we propose a novel approach to measure the fit degree of customer needs-product feature, which can effectively improve the performance of recommendation method. Our study also indicates the following findings: (1) UGC can be used to predict user ratings with no user rating records. This finding has important implications to solve the sparsity problem of recommendations thoroughly. (2) The customer need-based recommendation method has better performance than existing user preference-based recommendation methods. This finding sheds light on the necessity of mining customer need for recommendation methods. (3) UGC can be used to mine customer need and product features. This finding indicates that UGC also can be used in the other studies requiring information about customer need and product feature. (4) Comparing the opinions of user review should not be solely on the basis of semantic similarity. This finding sheds light on the limitation of existing opinion mining studies.

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.

Top-N item recommendation is one of the important tasks of recommenders. Collaborative filtering is the most popular approach to building recommender systems which can predict ratings for a given user and item. Collaborative filtering can be extended for top-N recommendation, but this approach does not work accurately for cold start users that have rated only a very small number of items. In this paper we propose novel methods exploiting a trust network to improve the quality of top-N recommendation. The first method performs a random walk on the trust network, considering the similarity of users in its termination condition. The second method combines the collaborative filtering and trust-based approach. Our experimental evaluation on the Epinions dataset demonstrates that approaches using a trust network clearly outperform the collaborative filtering approach in terms of recall, in particular for cold start users.

The rating prediction from a user to a product is one of the main reference of the recommendation systems widely used in E-commerce. The collaborative filtering algorithm is one of the most wildly used and successful approaches to predict the rating data. However, the rating data from many real-world scenarios is often sparse due to the difficulties of collection. How to improve the rating prediction algorithm with a sparse rating data becomes a real challenging problem. In this paper, we proposes an algorithm called UAD (User Attention Degree) based on the relationships between the number of rating times and the user attention. The algorithm predicts the user’s rating on an item taking account the impact from that user’s attention degree. The experimental results indicate that our algorithm can effectively improve the prediction accuracy.

How did the economic crisis impact social capital in European societies? The empirical studies conducted so far provide contradictory conclusions about the strength and direction of its influence. We argue that to better understand the effects of the economic crisis on social capital (social trust, formal and informal networks) it is crucial to examine both its impact on people’s economic situation and the way it reshaped the relationship between individuals and political institutions and altered key political factors (political trust, the welfare state, political activism). Our analysis of European Social Survey data between 2006 and 2012 shows that changes in social trust were smaller than in formal and informal social networks. It also confirms that political factors played an important mediating role in producing these changes: changes in social trust and formal networks can especially be explained by the impact of the political factors, while variations in informal networks are mainly due to the changing economy. Moreover, the analyses show that while the economic crisis generally lowered social capital, some mechanisms such as a sense of togetherness and left-wing political activism, enhanced social capital.

Traditionally, it is often assumed that data sparsity is a big problem of user-based collaborative filtering algorithm. However, the analysis is based only on data quantity without considering data quality, which is an important characteristic of data, sparse high quality data may be good for the algorithm, thus, the analysis is one-sided. In this paper, the effects of training ratings with different levels of sparsity on recommendation quality are first investigated on a real world dataset. Preliminary experimental results show that data sparsity can have positive effects on both recommendation accuracy and coverage. Next, the measurement of data noise is introduced. Then, taking data noise into consideration, the effects of data sparsity on the recommendation quality of the algorithm are re-evaluated. Experimental results show that if sparsity implies high data quality (low noise), then sparsity is good for both recommendation accuracy and coverage. This result has shown that the traditional analysis about the effect of data sparsity is one-sided, and has the implication that recommendation quality can be improved substantially by choosing high quality data.

(Purpose/Significance). This paper aims at the problems of inaccurate recommendation effect caused by data sparseness and cold start in the traditional collaborative filtering-based book personalized recommendation algorithm. So this paper proposes a collaborative filtering recommendation algorithm which improves the similarity solution method and the filling method of missing data. (Method/Process). By considering the influence of the user's common rating book collection on the similarity calculation, the average rating value of all books is used as the threshold, and the user's common rating weight is introduced into the user's similarity calculation. As for data filling, according to the user's average rating, the basic attributes such as the age and gender of users are coded, and then Euclidean distance is initially calculated, making hierarchical clustering on users. What's more, Shope-one algorithm is used to calculate the filling value of the former m similar users，and add the weight value of the degree simultaneously to get the final filling value, so as to improve the data filling method. (Result/Conclusion). Experiments were carried out with the data set of Book-Crossing Data set through Python. The experimental results show that the improved collaborative filtering algorithm has a significantly improvement in the accuracy and quality of book recommendation.

Social cohesion, neighbourhood resilience, and health: evidence from New Deal for Communities programme