Making recommendations from top-N user-item subgroups

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.

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.

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.

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

In the collaborative filtering algorithm, the user similarity and the selection of the nearest neighbor are the two most important links in the whole algorithm. The traditional collaborative filtering algorithm only relies on user ratings to calculate the similarity between users and then find each user’s K neighbors, and finally according to the K neighbor set recommended. However, in the face of large data processing, the traditional collaborative filtering algorithm is sparsely populated, resulting in the recommendation which is not obvious. A collaborative filtering algorithm for hybrid similarity is proposed for this problem. The algorithm is mainly from the user similarity degree of similarity, user attribute similarity and similarity of user purchase records, and many experiments to adjust the weight between them, the use of dynamic selection of the collection method to find the user’s nearest neighbor, so as to users Recommend more suitable items, improve the recommended quality of the algorithm. The experimental results show that the method proposed in this paper has lower MAE value than the traditional co-filtering algorithm, and the recommended quality is improved.

Collaborative and content-based filtering are the majorly used approaches towards implementing recommender systems which can successfully predict the item(s) to be recommended to users based upon their preferences. Each of the methods has its own advantages and disadvantages that are specific to the situation they are used in. The hybrid approach aims to combine both techniques in multiple ways to optimize results and overcome shortcomings. In this paper, we propose a hybrid approach based recommendation engine to improve accuracy. While content based recommendations are made on the similarity of items’ attributes, collaborative recommendations are based on similarity of users. Collaborative filtering is achieved by matrix factorization technique. One of the most effective algorithms for matrix factorization: Singular Value Decomposition (SVD) has been used to perform collaborative filtering and combined with content based model to predict the item ratings per user. The paper aims to achieve the final result with minimum possible errors as per user preferences and recommended items.

Collaborative filtering (CF), a technique used by recommendation systems, predicts and recommends items (information, products or services) that the user might like. Amazon.com's recommender system is one of the most famous examples of CF. Recommendation systems are popular in both commercial and research sectors, and they are applied in a variety of applications such as movies, music, books, social connections and venues. In particular, movie recommendation systems produce personal recommendations for movies. Existing CF algorithms employed in movie recommendation systems predict the unknown rating of a given user for a movie using only the ratings (i.e., preferences) of other like-minded users who have seen the movie. In such approaches, there exist certain limits in improving the accuracy of recommendation systems. This paper proposes an algorithm for movie recommendation that exploits the genre of the movie to enhance the accuracy of rating predictions. The proposed algorithm 1) numerically measures the correlation between movie genres using movie rating information; 2) classifies movies using the genre correlations and generates a list of recommended movies for the target user with the classified movies; and finally 3) predicts the ratings of the movies in the list using traditional CF algorithms. The experimental results show that the proposed algorithm yields higher accuracy in movie rating predictions than existing movie recommendation algorithms.

Collaborative Filtering recommendation is one of the most widely used recommendation systems in e-commerce recommendation systems. However, because of the growing number of users and goods, the recommended quality of traditional collaborative filtering technology is getting lower and lower. In this paper, we propose a method of Similarity Measurement Based on User - Attribute and User - Score to improve the accuracy of similarity calculation between users. First, this method calculates the similarity of user attribute and the similarity of user rating. Next, we combine the two similarities into the new similarity measure based on weighted fusion. Finally, we incorporate this similarity measure into the traditional collaborative filtering algorithm and improve the recommended quality. The experimental results show that the presented algorithm can improve the recommendation accuracy and produce a better recommendation results.

Recently, collaborative filtering (CF) algorithms have played an important role in recommendation systems. However, the traditional CF algorithms cannot make full use of information (e.g., users' preference, social relationship, and so on) implied in check-ins of users in the recommendation process. In order to address this issue, in this paper, a recommendation framework is proposed by exploiting historical user behavior, the check-in information and user social relationships to improve the precision of recommendation, as follows. First, user reputation can be obtained by iteratively calculating the correlation of historical ratings of the user and intrinsic qualities of items. Second, we employ user reputation and rating similarity in the basic social recommender model. Finally, we use the social connections among users to obtain the social influence of their friends. To evaluate the performance of our method, a series of experiments conducted on Epinions and Ciao data sets show that our approach performs better than several state-of-the-art methods on metrics of RMSE, precision rate, recall rate and F-Measure.

Collaborative Filtering (CF) Recommendation System is a prominent technology which is widely online. Large variety of available CF algorithms and the multitude of their possible parameters have a huge impact on quality of the outcome on ECommerce. Unfortunately, the literature on CF recommender system evaluation presents different evaluation metrics at different situation and could not provide any suggestion about the best. At the same time, it is a fact to accept that the accuracy measure should be different for each CF algorithm and depends upon the classification accuracy of that particular algorithm. As an initiative to address this problem in the present research paper, predictive accuracy metrics, classification accuracy metrics and rank accuracy metrics are considered as the classification accuracy metrics to know the overall competence for significant features of the chosen CF algorithms. At this juncture, normalization, a distinctive evaluation methodology, has been adopted to accomplish unique evaluation results of recommender systems. In this research paper, different accuracy metrics assessment would be brought into a common scale by taking into consideration of normalization process to evaluate metrics of the CF algorithms. A comprehensive comparative analysis is carried out and tabulated.

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.

Evaluation Of Recommendation System On A Movie Dataset

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.

Abstract—Recommender systems are often used to provide useful recommendations for users. They use previous history of the users-items interactions, e.g. purchase history and/or users rating on items, to provide a suitable recommendation list for any target user. They may also use contextual information available about items and users. Collaborative filtering algorithm and its variants are the most successful recommendation algorithms that have been applied to many applications. Collaborative filtering method works by first finding the most similar users (or items) for a target user (or items), and then building the recommendation lists. There is no unique evaluation metric to assess the performance of recommendations systems, and one often choose the one most appropriate for the application in hand. This paper compares the performance of a number of well-known collaborative filtering algorithms on movie recommendation. To this end, a number of performance criteria are used to test the algorithms. The algorithms are ranked for each evaluation metric and a rank aggregation method is used to determine the wining algorithm. Our experiments show that the probabilistic matrix factorization has the top performance in this dataset, followed by item-based and user-based collaborative filtering. Non-negative matrix factorization and Slope 1 has the worst performance among the considered algorithms.
 Keywords—Social networks analysis and mining, big data, recommender systems, collaborative filtering.