Reliable Collaborative Filtering on Spatio-Temporal Privacy Data

The development of location-based social networking (LBSN) services is growing rapidly these days. Users of LBSN services are more interested in sharing tips and experiences of their visits to various locations. In this paper, we aim at a study of recommending locations to users on LBSNs by collaborative filtering (CF) recommenders based only on users' check-in data. We first design and develop a distributed crawler to collect a large amount of check-in data from Gowalla. Then, we use three ways to utilize the check-in data, namely, the binary utilization, the FIF utilization, and the probability utilization. According to different utilizations, we introduce different CF recommenders, namely, user-based, item-based and probabilistic latent semantic analysis (PLSA), to do the location recommendation. Finally, we conduct a set of experiments to compare the performances of different recommenders using different check-in utilizations. The experimental results show that PLSA recommender with the probability utilization outperforms other combinations of recommenders and utilizations for recommending locations to users on LBSN.

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.

With the rapidly growing location-based social networks (LBSNs), personalized geo-social recommendation becomes an important feature for LBSNs. Personalized geo-social recommendation not only helps users explore new places but also makes LBSNs more prevalent to users. In LBSNs, aside from user preference and social influence, geographical influence has also been intensively exploited in the process of location recommendation based on the fact that geographical proximity significantly affects users' check-in behaviors. Although geographical influence on users should be personalized, current studies only model the geographical influence on all users' check-in behaviors in a universal way. In this paper, we propose a new framework called iGSLR to exploit personalized social and geographical influence on location recommendation. iGSLR uses a kernel density estimation approach to personalize the geographical influence on users' check-in behaviors as individual distributions rather than a universal distribution for all users. Furthermore, user preference, social influence, and personalized geographical influence are integrated into a unified geo-social recommendation framework. We conduct a comprehensive performance evaluation for iGSLR using two large-scale real data sets collected from Foursquare and Gowalla which are two of the most popular LBSNs. Experimental results show that iGSLR provides significantly superior location recommendation compared to other state-of-the-art geo-social recommendation techniques.

Collaborative filtering, which makes personalized predictions by learning the historical behaviors of users, is widely used in recommender systems. It makes the prediction and recommend by similarity of users, and it can handle the various work. But the traditional collaborative filtering ignores the connection of users and items. Affect the recommendation's results. To find similar users by measuring the customer relationship between the neighbors can improve the accuracy of prediction user interests'. Then it can improve the accuracy of the recommendation. So collaborative filtering recommendation algorithm based on MDP model is proposed. It can find the connection of users purchase and next purchase. So it can predict users next purchase. Then can recommend items to users. The test results shows the algorithm of this paper have more accuracy.

Trust-aware location recommendation in location-based social networks: A graph-based approach

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.

A Contextual Recurrent Collaborative Filtering framework for modelling sequences of venue checkins

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.

Collaborative Filtering (CF) is a successful technology that has been implemented in E-commerce recommender systems. However, the risks of shilling attacks have already aroused increasing concerns of the society. Current solutions mainly focus on attack detection methods and robust CF algorithms that have flaws of unassured prediction accuracy. Furthermore, attack detection methods require a threshold to distinguish normal users from fake users and suffer from the problems of false positive if the threshold is too high and false negative if too low. This paper proposes a soft-decision method, Neighbor Selection with Variable-Length Partitions (VLPNS), to reduce false positive rate through marking suspicious fakers instead of deleting them directly such that misclassified normal users can still contribute to the similarity calculation. The method works as follows: First, it gets user's suspicion probability by applying SVM. It then generates partitions of variable sizes from which different numbers of neighbors can be selected by using the bisecting c-means clustering algorithm. Finally, it chooses neighbors considering the user's suspicion degree and similarity with target user at the same time. Theoretical and experimental analysis show that our approach ensures an excellent prediction accuracy against shilling attacks.

Personalized location recommendation is a special topic of recommendation. It is related to human mobile behavior in the real world regarding various contexts including spatial, temporal, social, and content. The development of this topic is subject to the availability of human mobile data. The recent rapid growth of location-based social networks has alleviated such limitation, which promotes the development of various location recommendation techniques. This tutorial offers an overview, in a data mining perspective, of personalized location recommendation on location-based social networks. It introduces basic concepts, summarizes unique LBSN characteristics and research opportunities, elaborates associated challenges, reviews state-of-the-art algorithms with illustrative examples and real-world LBSN datasets, and discusses effective evaluation methods.

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.

Recent years have witnessed the ever-growing popularity of location-based social network (LBSN) services. Top-N place recommendation, which aims at retrieving N most appealing places for a target user, has thus gained increasing importance. Yet existing solutions to this problem either provide non-personalized recommendations by selecting nearby popular places, or resort to collaborative filtering (CF) by treating each place as an independent item, overlooking the geographical and semantic correlations among places. In this paper, we propose GoTo, a collaborative recommender that provides top-N personalized place recommendation in LBSNs. Compared with existing methods, GoTo achieves its effectiveness by exploiting the wisdom of the so-called local experts, namely those who are geographically close and have similar preferences with regard to a certain user. At the core of GoTo lies a novel user similarity measure called geo-topical similarity, which combines geographical and semantic correlations among places for discovering local experts. In specific, the geo-topical similarity uses Gaussian mixtures to model users’ real-life geographical patterns, and extracts users’ topical preferences from the attached tags of historically visited places. Extensive experiments on real LBSN datasets show that compared with baseline methods, GoTo can improve the performance of top-N place recommendation by up to 50% in terms of accuracy.

Point-Of-Interest (POI) recommendation systems suggest interesting locations to users based on their previous check-ins via location-based social networks (LBSNs). Individuals visiting a location are partially affected by many factors including social links, travel distance and the time. A growing line of research has been devoted to taking advantage of various effects to improve existing location recommendation methods. However, the temporal influence owns numerous dimensions which deserve to be explored more in depth. The subset property comprises a set of homogeneous slots such as an hour of the day, the day of the week, week of the month, month of the year, and so on. In addition, time has other attributes such as the recency which signifies the newly visited locations versus others. In this paper, we further study the role of time factor in recommendation models. Accordingly, we define a new problem to jointly model a pair of heterogeneous time-related effects (recency and the subset feature) in location recommendation.

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.

The paper builds an evaluation model of user interest based on resource multi-attributes, proposes a modified Pearson-Compatibility multi-attribute group decision-making algorithm, and introduces the algorithm to solve the recommendation problem of k-neighbor similar users. Considering the characteristics of collaborative filtering recommendation, the paper addresses the issues on the preference differences of similar users, incomplete values, and advanced converge of the algorithm. Thus the paper realizes multi-attribute collaborative filtering. Finally, the effectiveness of the algorithm is proved by an experiment of collaborative recommendation among multi-users based on virtual environment.