Improved movie recommendations based on a hybrid feature combination method

Recommender systems help users find relevant items efficiently based on their interests and historical interactions with other users. They are beneficial to businesses by promoting the sale of products and to user by reducing the search burden. Recommender systems can be developed by employing different approaches, including collaborative filtering (CF), demographic filtering (DF), content-based filtering (CBF) and knowledge-based filtering (KBF). However, large amounts of data can produce recommendations that are limited in accuracy because of diversity and sparsity issues. In this paper, we propose a novel hybrid method that combines user–user CF with the attributes of DF to indicate the nearest users, and compare four classifiers against each other. This method has been developed through an investigation of ways to reduce the errors in rating predictions based on users’ past interactions, which leads to improved prediction accuracy in all four classification algorithms. We applied a feature combination method that improves the prediction accuracy and to test our approach, we ran an offline evaluation using the 1M MovieLens dataset, well-known evaluation metrics and comparisons between methods with the results validating our proposed method

From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks

Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyber-attacks has been increasing over the years and damages have been caused. Furthermore, graphs can be used to show paths than can be exploited by attackers to intrude into systems and gain unauthorized access through vulnerability exploitation. This paper presents a method that builds attack graphs using data supplied from the maritime supply chain infrastructure. The method delivers all possible paths that can be exploited to gain access. Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks. The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks in terms of risk management. The proposed method has been experimentally evaluated and validated, with the results showing that it is both practical and effective

Reproducibility of experiments in recommender systems evaluation

© IFIP International Federation for Information Processing 2018 Published by Springer International Publishing AG 2018. All Rights Reserved. Recommender systems evaluation is usually based on predictive accuracy metrics with better scores meaning recommendations of higher quality. However, the comparison of results is becoming increasingly difficult, since there are different recommendation frameworks and different settings in the design and implementation of the experiments. Furthermore, there might be minor differences on algorithm implementation among the different frameworks. In this paper, we compare well known recommendation algorithms, using the same dataset, metrics and overall settings, the results of which point to result differences across frameworks with the exact same settings. Hence, we propose the use of standards that should be followed as guidelines to ensure the replication of experiments and the reproducibility of the results

A switching multi-level method for the long tail recommendation problem

Recommender systems are decision support systems that play an important part in generating a list of product or service recommendations for users based on the past experiences and interactions. The most popular recommendation method is Collaborative Filtering (CF) that is based on the users’ rating history to generate the recommendation. Although, recommender systems have been applied successfully in different areas such as e-Commerce and Social Networks, the popularity bias is still one of the challenges that needs to be further researched. Therefore, we propose a multi-level method that is based on a switching approach which solves the long tail recommendation problem (LTRP) when CF fails to find the target case. We have evaluated our method using two public datasets and the results show that it outperforms a number of bases lines and state-of-the-art alternatives with a further reduce of the recommendation error rates for items found in the long tail

The First Caltech-Jodrell Bank VLBI Survey. III. VLBI and MERLIN Observations at 5 GHz and VLA Observations at 1.4 GHz

We present the 5 GHz results from the first Caltech-Jodrell Bank (CJ1) VLBI survey. The 1.6 GHz maps were presented in two separate papers (Polatidis et al. 1995; Thakkar et al. 1995). These three papers complete the first stage of this program to map at both 1.6 and 5 GHz all objects accessible to Mark II VLBI in the complete sample of 135 objects with 1.3 > S_(5 GHz) ≥ 0.7 Jy, δ(1950) ≥ 35°, and |b| > 10°. The combination of the CJ1 sample with the Pearson-Readhead (PR) sample provides a complete, flux density-limited sample of 200 objects with S_(5 GHz) ≥ 0.7 Jy, δ(1950) ≥35°, and |b| > 10° for which all of the objects accessible to Mark II VLBI have been mapped at both 5 GHz(129 objects) and 1.6 GHz(132 objects). In addition to the 5 GHz VLBI maps, we present in this paper 5 GHz MERLIN observations of 20 objects and 1.4 GHz VLA observations of 92 objects in the combined CJ1 + PR sample. The VLA maps, together with L- band (1.3-1.7 GHz) maps available in the literature, provide a complete set of VLA maps for the combined CJ1 + PR sample. Finally, we present the radio spectra of the objects in the CJ1 sample. The combined CJ1 + PR VLBI surveys provide a sample which is large enough for a number of important astrophysical and cosmological studies. These will be presented in further papers in this series

Two-sided ejection in powerful radio sources: The compact symmetric objects

We present very long base interferometry (VLBI) images of the compact high-luminosity radio galaxy 2352+495 that show symmetric structure on either side of a prominent central core. This contrasts strongly with the asymmetric nuclear structure exhibited by the great majority of powerful extragalactic sources. The outer structure of 2352+495 takes the form of two 'mini-lobes' containing hot spots; in this respect this compact radio galaxy resembles extended radio galaxies, but its overall size, ~150 pc, is ~1000 times smaller. A reanalysis of existing data on the radio galaxy 0710+439 shows similar compact structure, and together these VLBI images confirm the existence of a class of two-sided compact symmetric objects (CSOs). We show that, in contrast to nuclear radio sources in other powerful objects, the observed structure of CSOs is not dominated by relativistic beaming effects. It is likely that many objects previously classified as 'compact doubles' will prove to be CSOs when mapped with VLBI with high dynamic range

Caltech-Jodrell Bank (CJ) VLBI Snapshot Surveys

Two large VLBI surveys are currently underway which utilise the snapshot technique pioneered on the VLA. With a 12–16 telescope array three ~ 20 min snapshots are sufficient to make excellent hybrid maps. Recent advances in data analysis techniques enable surveys of several hundred sources to be undertaken and reduced in under two years