A Hybrid Recommendation System Based on Density-Based Clustering

Memory-based collaborative filtering (CF) techniques have been widely implemented for predicting ratings to unseen items by aggregating ratings of similar users or items in recommender systems (RS). Usually, sufficient ratings from similar users or similar items are not available in the rating matrix, due to the data sparsity problem. Further, these techniques suffer from correlation based problems inherent in used similarity measures. Consequently, higher prediction accuracy cannot be achieved. In this paper, we propose the use of fuzzy Naive Bayesian (FNB) classifier for user based CF and item based CF for implicitly computing similarity between users as well as items on the basis of conditional probabilities and develop fuzzy Naive Bayesian classifier to user based CF (FNB-UB-CF) and item based CF (FNB-IB-CF). We further develop a hybrid RS (FNB-UB-IB-CF) by combining the proposed FNB-UB-CF and FNB-IB-CF. Their combinations would be helpful in alleviating the sparsity because both user ratings and item ratings are employed. Experimental results demonstrate that the proposed methods are indeed more robust against data sparsity and give better recommendation quality using a popular MovieLens dataset.

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, the most successful recommender system technology to date, helps people make choices based on the opinions of other people. Existing collaborative filtering methods, mainly user-based and item-based methods, predict new ratings by aggregating rating information from either similar users or items. However, a large amount of ratings of similar items or similar users may be unavailable because of the sparse characteristic inherent to the rating data. For this reason, we present a Hybrid Predictive Algorithm with Smoothing (HSPA). HSPA uses item-based methods to provide the basis for data smoothing and builds the predictive model based on both users? aspects and items? aspects in order to ensure robust to data sparsity and predictive accuracy. Moreover, HSPA utilizes the user clusters to achieve high scalability. Experimental results from real datasets show that HSPA effectively contributes to the improvement of prediction on sparse data.

One important problem of recommender system is rating prediction. In this paper, we use the movie rating data from MovieLens as an example to show how to use users’ attributes to improve the accuracy of rating prediction. Through data analysis, we observe that users having similar attributes tend to share more similar preferences and users with a special attribute have their own preferred items. Based on the two observations, we assume that a user’s rating to an item is determined by both the user intrinsic characteristics and the user common characteristics. Using the widely adopted latent factor model for rating prediction, in our proposed solution, we use two kinds of latent factors to model a user: one for the user intrinsic characteristics and the other for the user common characteristics. The latter encodes the influence of users’ attributes which include user age, gender and occupation. On the other hand, we jointly use user attributes or item category information and rating data for calculating similarity of users or items. The similarity calculating results are used in our proposed latent factor model as a regularization term to regularize users or items latent factors gap. Experimental results on MovieLens show that by incorporating users’ attributes influences, much lower prediction error is achieved than the state-of-the-art models. The prediction error is further reduced by incorporating influences from item category popularity and item popularity.

Memory-based collaborative filtering (CF) aims at predicting the rating of a certain item for a particular user based on the previous ratings from similar users and/or similar items. Previous studies in finding similar users and items have several drawbacks. First, they are based on user-defined similarity measurements, such as Pearson Correlation Coefficient (PCC) or Vector Space Similarity (VSS), which are, for the most part, not adaptive and optimized for specific applications and data. Second, these similarity measures are restricted to symmetric ones such that the similarity between A and B is the same as that for B and A, although symmetry may not always hold in many real world applications. Third, they typically treat the similarity functions between users and functions between items separately. However, in reality, the similarities between users and between items are inter-related. In this paper, we propose a novel unified model for users and items, known as Similarity Learning based Collaborative Filtering (SLCF) , based on a novel adaptive bidirectional asymmetric similarity measurement. Our proposed model automatically learns asymmetric similarities between users and items at the same time through matrix factorization. Theoretical analysis shows that our model is a novel generalization of singular value decomposition (SVD). We show that, once the similarity relation is learned, it can be used flexibly in many ways for rating prediction. To take full advantage of the model, we propose several strategies to make the best use of the proposed similarity function for rating prediction. The similarity can be used either to improve the memory-based approaches or directly in a model based CF approaches. In addition, we also propose an online version of the rating prediction method to incorporate new users and new items. We evaluate SLCF using three benchmark datasets, including MovieLens, EachMovie and Netflix, through which we show that our methods can outperform many state-of-the-art baselines.

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.

Recommender systems aim to provide users with preferred items to tackle the information overload problem in the Web era. Social relations, item connections, and usergenerated reviews on items contain abundant potential information. By combining matrix factorization with latent Dirichlet allocation, we integrate ratings, reviews, user similarity and item similarity in recommender systems. The experimental result on a real-world dataset proves that both item connection and user connection contain useful sources for recommendation, and our model can effectively improve recommendation quality.

In the social network, similar users are assumed to prefer similar items, so searching the similar users of a target user plays an important role for most collaborative filtering methods. Existing collaborative filtering methods use user ratings of items to search for similar users. Nowadays, abundant social information is produced by the Internet, such as user profiles, social relationships, behaviors, interests, and so on. Only using user ratings of items is not sufficient to recommend wanted items and search for similar users. In this paper, we propose a new collaborative filtering method using social information fusion. Our method first uses social information fusion to search for similar users and then updates the user rating of items for recommendation using similar users. Experiments show that our method outperforms the existing methods based on user ratings of items and using social information fusion to search similar users is an available way for collaborative filtering methods of recommender systems.

Recently, there is a surge of social recommendation, which leverages social relations among users to improve recommendation performance. However, in many applications, social relations are absent or very sparse. Meanwhile, the attribute information of users or items may be rich. It is a big challenge to exploit these attribute information for the improvement of recommendation performance. In this paper, we organize objects and relations in recommendation system as a heterogeneous information network, and introduce meta path based similarity measure to evaluate the similarity of users or items. Furthermore, a matrix factorization based dual regularization framework SimMF is proposed to flexibly integrate different types of information through adopting the similarity of users and items as regularization on latent factors of users and items. Extensive experiments not only validate the effectiveness of SimMF but also reveal some interesting findings. We find that attribute information of users and items can significantly improve recommendation accuracy, and their contribution seems more important than that of social relations. The experiments also reveal that different regularization models have obviously different impact on users and items.

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.

Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based filtering approach tries to recommend items that are similar to new users' profiles. The fundamental issues include how to profile new users, and how to deal with the over-specialization in content-based recommender systems. Indeed, the terms used to describe items can be formed as a concept hierarchy. Therefore, we aim to describe user profiles or information needs by using concepts vectors. This paper presents a new method to acquire user information needs, which allows new users to describe their preferences on a concept hierarchy rather than rating items. It also develops a new ranking function to recommend items to new users based on their information needs. The proposed approach is evaluated on Amazon book datasets. The experimental results demonstrate that the proposed approach can largely improve the effectiveness of recommender systems.

Collaborative filtering (CF) is the most popular recommendation technique nowadays. Traditional CF approaches compute a similarity value between the target user and each other user by computing the relativity of their rating style, which is the set of ratings given on the same items. Based on the ratings of the most similar users, commonly referred to as neighbors, CF algorithms compute recommendations for the target user. The problem with this approach is that the similarity value is only considering the user-item ratings. To solve this problem, this paper combining the item attribute similarity and the item rating similarity, which takes into account the influence of item information and user rating to enhance the item-based CF. The experimental results show that the algorithm combined the item attribute similarity and the item rating similarity is promising, since it does not only solve the dataset sparsity problem of recommender systems, but also assists in increasing the accuracy of systems employing it.

Identification and assessment of sites for solar farms development using GIS and density based clustering technique- A case of Pakistan

Future self-driving cars and current ones with advanced driver assistance systems are expected to interact with other traffic participants, which often are multiple other vehicles. Object vehicle tracking forms a key part of resolving this interaction. Furthermore, descriptions of the vehicle group behaviors, like group formations or splits, can enhance the utility of the tracking information for further motion planning and control decisions. In this paper, we propose a probabilistic method to estimate the formation and evolution, including splitting, re-grouping, and so on, of object vehicle groups and the membership conditions for individual object vehicles forming the groups. A Bayesian estimation approach is used to first estimate the states of the individual vehicles in the presence of uncertainties due to sensor imperfections and other disturbances acting on the individual object vehicles. The closeness of the individual vehicles in both their positions and velocity is then evaluated by a probabilistic collision condition. Based on this, a density-based clustering approach is applied to identify the vehicle groups as well as the identity of the individual vehicles in each group. An estimation of the state of the group as well as of the group boundary is also given. Finally, detailed numerical experiments are included, including one on real-time traffic intersection data, to illustrate the workings and the performance of the proposed approach. The potential application of the approach in motion planning of autonomous vehicles is also highlighted.

The unveiling of communities within a network or graph, and the hierarchization of its members that results is of utmost importance in areas ranging from social to biochemical networks, from electronic circuits to cybersecurity. We present a statistical mechanics approach that uses a normalized Gaussian function which captures the impact of a node within its neighborhood and leads to a density-ranking of nodes by considering the distance between nodes as punishment. A hill-climbing procedure is applied to determine the density attractors and identify the unique parent (leader) of each member as well as the group leader. This organization of the nodes results in a tree-like network with multiple clusters, the community tree. The method is tested using synthetic networks generated by the LFR benchmarking algorithm for network sizes between 500 and 30,000 nodes and mixing parameter between 0.1 and 0.9. Our results show a reasonable agreement with the LFR results for low to medium values of the mixing parameter and indicate a very mild dependence on the size of the network.