Recommender systems

In this paper, we focus on improving the performance of recommender systems. To do this, we propose a new algorithm named PBloofI which is a kind of hierarchical bloom filter. Actually, the Bloom filter is an array-based technique for showing the items’ features. Since the feature vectors of items are sparse, the Bloom filter reduces the space usage by using the hashing technique. And also, to reduce the time complexity we used the hierarchical version of bloom filter which is based on B+ tree of order d. Since Bloom filters can make a tradeoff between space and time, proposing a new hierarchical Bloom filter causes a remarkable reduction in space and time complexity of recommender systems. To increase the accuracy of the recommender systems we use Probabilistic version of hierarchical Bloom filter. By measuring the accuracy of the algorithm we show that the proposed algorithms not only decrease the time complexity but also have no significant effect on accuracy.

Nowadays, we deal with a large volume of information that we may have wrong choices without appropriate guidance. To this end, recommender systems are proposed which are a type of information filtering system that acts as a filter and displays information that is useful and close to the user's interests. They reduce the volume of the retrieved information and help users to select relevant products from millions of choices available on the internet. However, since these systems use explicitly and implicitly collected information about the user's interests for different items to predict the user's favorite items, the adversaries due to their openness nature might attack them. Therefore, identifying them is essential to improve the quality of the recommendations. For this purpose, in this paper, a method based on two criteria of a maximum number of users with the equal length and the degree of novelty of their profiles is presented and finally, the DBSCAN clustering algorithm is used to distinguish genuine users from fake users. In order to improve the DBSCAN algorithm, we proposed a new method to determine the values of Eps and MinPts automatically. The results of the proposed method are compared with a new comparative study on shilling detection methods for trustworthy recommendations, which shows that the proposed method independent of the type of attack can identify fake users in most cases with accuracy close to 1.

Purpose: The main purpose of this study is to design and evaluate a book recommender system in digital and public libraries. The solution has been provided by receiving and reviewing the preferences and experiences of users and profile information and studying the background of each user, as well as considering groups of features recorded in the recommendation process. Method: This research is applied in terms of purpose and survey method. The statistical population studied in this research consists of 263 questionnaires of users and 30 questionnaires of librarian experts. In order to find similarity between users and books, clustering and grouping have been used. Findings: There are two criteria for grouping: users grouping that can be used on the three indicators of age, gender, educational level, and thematic classification of books can be based on scope, branch, and sub-category. In analyzing the data in the descriptive statistics section, Excel software is used and in the analytical section, SPSS software. Findings indicate that the accuracy criterion has been improved by calculating MAE and RSME in the proposed method compared to the basic method in this field. The results also showed that classification can have a significant impact on the forecast and performance of book forecasting systems. Conclusion: The evaluation of the conceptual design showed that by focusing on user characteristics and obtaining real feedback of Iranian libraries, the recommender can serve as a key and effective element in the service of the Iranian readership community and play a good role as a virtual reference librarian.