Background and Objectives: The primary purpose of recommender systems is to estimate the users' desires and provide a predicted list of items based on relevant data. Recommender systems that suggest items to users face two Cold Start and sparse data challenges. Methods: This paper aims to propose a novel method to overcome such challenges in recommender systems. Singular value decomposition is a popular method to reduce sparse data in recommender systems by reducing dimensions. However, the basic singular value decomposition can only extract those feature vectors of users and items that may be recommended with lower recommendation precisions. Notably, using the similarity criteria between entities can reduce Cold Start to resolve the singular value decomposition problem by extracting more refined factor vectors. Besides, considering the context's dimensions as the third dimension of the matrix requires using another flexible algorithm, such as tensor factorization, which offers a viable solution to minimize the sparse data challenge. This study proposes TCSSVD, a novel method to resolve the challenges mentioned above in recommender systems. First, a two-level matrix is obtained using the similarity criteria between the user and the item to reduce the Cold Start challenge. In the second step, the contextual information is used by tensor in two-level singular value decomposition to reduce the challenge of sparse data. Results: For reviewing the proposed method, these two data sets, IMDB and STS, were used because of applying user and item features and contextual information. The RMSE criterion (95% accuracy) was used to investigate the predictions' accuracy. However, since the user's rating of the item is particularly important in recommender systems, compared with other methods, such as tensor factorization, HOSVD, BPR, and CTLSVD, the TCSSVD method uses the following criteria: Precision, Recall, F1-score, and NDCG. Conclusion: The findings indicated the positive effect of using the innovative similarity criteria on the extraction of user and item attributes to reduce the complications deriving from the Cold Start challenge. Also, the use of contextual information through the tensor in the TCSSVD method reduced the complications related to sparse data. The results improve the recommendation accuracy of the recommender systems.

Recommender systems have become significant tools in electronic commerce, proposing effectively those items that best meet the preferences of users. A variety of techniques have been proposed for the recommender systems such as, collaborative filtering and content-based filtering. This study proposes a new hybrid recommender system that focuses on improving the performance under the “new user Cold-Start” condition where existence of users with no ratings or with only a small number of ratings is probable. In this method, the optimistic exponential type of ordered weighted averaging (OWA) operator is applied to fuse the output of five recommender system strategies.Experiments using MovieLens dataset show the superiority of the proposed hybrid approach in the Cold-Start conditions.

Recommender system based on collaborative filtering (CF) suffers from two basic problems known as Cold Start and sparse data. Appling metric similarity criteria through matrix factorization is one of the ways to reduce challenge of Cold Start. However, matrix factorization extract characteristics of user vectors & items, to reduce accuracy of recommendations. Therefore, SSVD two-level matrix design was designed to refine features of users and items through NHUSM similarity criteria, which used PSS and URP similarity criteria to increase accuracy to enhance the final recommendations to users. In addition to compare with common recommendation methods, SSVD is evaluated on two real data sets, IMDB and STS. Experimental results depict that proposed SSVD algorithm performs better than traditional methods of User-CF, Items-CF, and SVD recommendation in terms of precision, recall, F1-measure. Our detection emphasizes and accentuate the importance of Cold Start in recommender system and provide with insights on proposed solutions and limitations, which contributes to the development.

Recommender systems are used in various fields such as movies, music, and social networks. Recommender systems aim to provide attractive offers to users according to their performance in the system. The most popular recommender systems are content-based models and collaborative filtering methods. One of the most important challenges and problems in recommender systems is the challenge of users' Cold Start. So far, various methods such as machine learning algorithms, optimization approaches, and statistical methods, have been proposed by other researchers in improving internet marketing strategy and overcoming the Cold-Start problem, which despite having numerous applications, still could not solve the Start problem. This article will investigate the problem of Cold Start users' by presenting a recommendation model based on a deep neural network and considering the problem of improving the internet (network) marketing strategy. In this article, the relevant simulation is done on the popular Movielens dataset, which is from 2015, and the evaluations of the methods presented on this dataset are compared

The movie recommendation systems are always faced with the new movie Cold Start problem. Nowadays, social media platform such as Twitter is considered as a rich source of information in various domains, like movies, motivated us to exploit Twitter's content to tackle the movie Cold Start problem. In this study, we propose a hybrid movie recommendation method utilizing microblogs, movie features, and sentiment lexicon to reduce the effect of data sparsity. For this purpose, first, the movie features are extracted from the Internet Movie Database (IMDB), and the average IMDB score is calculated during the 7-days opening of the movie. Second, the related tweets of the movie and the cast are retrieved by the Twitter API. Third, the polarity of tweets and the public’ s feeling towards the target movie is extracted using sentiment lexicon analysis. Finally, the results of the three previous steps are integrated, and the prediction is obtained. Our results are compared with the sales volume of the target movie in 7-days opening, which is available in the Mojo Box office. In addition to the real-world benchmarking, we performed extensive experiments to demonstrate the accuracy and effectiveness of our proposed approach in comparison with the other state-of-the-art methods.

The spoofing subject is becoming ever increasingly more severe. In addition to the flow of technology, the availability of software-defined radio platforms has increased. Usually, detecting the spoofing is performed by introducing the features that are difficult for the deceiver to counterfeit. Spoofing and countering can be performed in different parts of a GPS receiver. In recent years, less attention has been paid to defense at Cold-Start. This research presents that the spoofing attack can be diminished during the initial Start-up process with a very short effective time. This low-cost method introduces a new decision rule based on a multiple statistical hypothesis test to identify fake peaks in correlation output of acquisition and extract the authentic peaks utilizing the wavelet transform or peak removal process. The main distinction of this method with previous works is investigating different amplitude ratios of spoofing signal to authentic. Simulation results on 10 data sets show that the probability of correct detection and mitigation is more than 90%.