Neural Collaborative Filtering Classification Model to Obtain Prediction Reliabilities

Neural collaborative filtering is the state of art field in the recommender systems area; it provides some models that obtain accurate predictions and recommendations. These models are regression-based, and they just return rating predictions. This paper proposes the use of a classification-based approach, returning both rating predictions and their reliabilities. The extra information (prediction reliabilities) can be used in a variety of relevant collaborative filtering areas such as detection of shilling attacks, recommendations explanation or navigational tools to show users and items dependences. Additionally, recommendation reliabilities can be gracefully provided to users: “probably you will like this film”, “almost certainly you will like this song”, etc. This paper provides the proposed neural architecture; it also tests that the quality of its recommendation results is as good as the state of art baselines. Remarkably, individual rating predictions are improved by using the proposed architecture compared to baselines. Experiments have been performed making use of four popular public datasets, showing generalizable quality results. Overall, the proposed architecture improves individual rating predictions quality, maintains recommendation results and opens the doors to a set of relevant collaborative filtering fields

Understanding Shilling Attacks and Their Detection Traits: A Comprehensive Survey

The internet is the home for huge volumes of useful data that is constantly being created making it difficult for users to find information relevant to them. Recommendation System is a special type of information filtering system adapted by online vendors to provide recommendations to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. Over the years, multiple attack models and detection techniques have been developed to mitigate the problem. This paper aims to be a comprehensive survey of the shilling attack models, detection attributes, and detection algorithms. Additionally, we unravel and classify the intrinsic traits of the injected profiles that are exploited by the detection algorithms, which has not been explored in previous works. We also briefly discuss recent works in the development of robust algorithms that alleviate the impact of shilling attacks, attacks on multi-criteria systems, and intrinsic feedback based collaborative filtering methods

Building Up Recommender Systems By Deep Learning For Cognitive Services

Cognitive services provide artificial intelligence (AI) technology for application developers, who are not required to be experts on machine learning. Cognitive services are presented as an integrated service platform where end users bring abilities such as seeing, hearing, speaking, searching, user profiling, etc. to their own applications under development via simple API calls. As one of the above abilities, recommender systems serve as an indispensable building brick, especially when it comes to the information retrieval functionality in the cognitive service platform. This thesis focuses on the novel recommendation algorithms that are able to improve on recommendation quality measured by accuracy metrics, e.g., precision and recall, with advanced deep learning techniques. Recent deep learning-based recommendation models have been proved to have state-ofthe-art recommendation quality in a host of recommendation scenarios, such as rating prediction tasks, top-N ranking tasks, sequential recommendation, etc. Many of them only leverage the existing information acquired from users’ past behaviours to model them and make one or a set of predictions on the users’ next choice. Such information is normally sparse so that an accurate user behaviour model is often difficult to obtain even with deep learning. To overcome this issue, we invent various adversarial techniques and apply them to deep learning recommendation models in different scenarios. Some of these techniques involve generative models to address data sparsity and some improve user behaviour modelling by introducing an adversarial opponent in model training. We empirically show the effectiveness of our novel techniques and the enhancement achieved over existing models via thorough experiments and ablation studies on widely adopted recommendation datasets. The contributions in this thesis are as follows: 1. Propose the adversarial collaborative auto-encoder model for top-N recommendation; 2. Propose a novel deep domain adaptation cross-domain recommendation model for rating prediction tasks via transfer learning; 3. Propose a novel adversarial noise layer for convolutional neural networks and a convolutional generative adversarial model for top-N recommendation

Blockchain-based recommender systems: Applications, challenges and future opportunities

Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and precise recommender systems. Such datasets often include sensitive information, yet most recommender systems are focusing on the models' accuracy and ignore issues related to security and the users' privacy. Despite the efforts to overcome these problems using different risk reduction techniques, none of them has been completely successful in ensuring cryptographic security and protection of the users' private information. To bridge this gap, the blockchain technology is presented as a promising strategy to promote security and privacy preservation in recommender systems, not only because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics. This paper presents a holistic review of blockchain-based recommender systems covering challenges, open issues and solutions. Accordingly, a well-designed taxonomy is introduced to describe the security and privacy challenges, overview existing frameworks and discuss their applications and benefits when using blockchain before indicating opportunities for future research. 2021 Elsevier Inc.This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Towards MANET-based Recommender Systems for Open Facilities

Nowadays, most recommender systems are based on a centralized architecture, which can cause crucial issues in terms of trust, privacy, dependability, and costs. In this paper, we propose a decentralized and distributed MANET-based (Mobile Ad-hoc NETwork) recommender system for open facilities. The system is based on mobile devices that collect sensor data about users locations to derive implicit ratings that are used for collaborative filtering recommendations. The mechanisms of deriving ratings and propagating them in a MANET network are discussed in detail. Finally, extensive experiments demonstrate the suitability of the approach in terms of different performance metrics. © 2021, The Author(s)

Evaluating collaborative filtering over time

Recommender systems have become essential tools for users to navigate the plethora of content in the online world. Collaborative filtering—a broad term referring to the use of a variety, or combination, of machine learning algorithms operating on user ratings—lies at the heart of recommender systems’ success. These algorithms have been traditionally studied from the point of view of how well they can predict users’ ratings and how precisely they rank content; state of the art approaches are continuously improved in these respects. However, a rift has grown between how filtering algorithms are investigated and how they will operate when deployed in real systems. Deployed systems will continuously be queried for personalised recommendations; in practice, this implies that system administrators will iteratively retrain their algorithms in order to include the latest ratings. Collaborative filtering research does not take this into account: algorithms are improved and compared to each other from a static viewpoint, while they will be ultimately deployed in a dynamic setting. Given this scenario, two new problems emerge: current filtering algorithms are neither (a) designed nor (b) evaluated as algorithms that must account for time. This thesis addresses the divergence between research and practice by examining how collaborative filtering algorithms behave over time. Our contributions include: 1. A fine grained analysis of temporal changes in rating data and user/item similarity graphs that clearly demonstrates how recommender system data is dynamic and constantly changing. 2. A novel methodology and time-based metrics for evaluating collaborative filtering over time, both in terms of accuracy and the diversity of top-N recommendations. 3. A set of hybrid algorithms that improve collaborative filtering in a range of different scenarios. These include temporal-switching algorithms that aim to promote either accuracy or diversity; parameter update methods to improve temporal accuracy; and re-ranking a subset of users’ recommendations in order to increase diversity. 4. A set of temporal monitors that secure collaborative filtering from a wide range of different temporal attacks by flagging anomalous rating patterns. We have implemented and extensively evaluated the above using large-scale sets of user ratings; we further discuss how this novel methodology provides insight into dimensions of recommender systems that were previously unexplored. We conclude that investigating collaborative filtering from a temporal perspective is not only more suitable to the context in which recommender systems are deployed, but also opens a number of future research opportunities

Identifying and minimizing the effects of malicious behavior in SERF

Collaborative filtering (CF) algorithms are used in a wide range of internet applications. However the chief objective of using CF algorithms across most of these applications is to discover items that might be of interest to its users. CF algorithms work by obtaining feedback from users on the items that they browse and utilize that feedback to suggest recommendations to other users with similar tastes. CF algorithms rely heavily on input provided by humans and thus it is vital to verify that this information is appropriate. In this paper, we analyze various mechanisms by which users can enter malicious data to a CF system called SERF (System for Electronic Recommendation Filtering). We explore how bad data can be propagated through the system and can be used to manipulate the quality of recommendations. We also explore some techniques to counter the effects of bad data on the system. We report the results of our experiment with two simulated systems - a reputation system that utilizes a user's agreement and disagreement history to predict the trust that can be attributed to a user and a word weighting scheme based on word co-occurrence.Keywords: malicious behavior, System for Electronic Recommendation Filtering, SERF, Collaborative filteringKeywords: malicious behavior, System for Electronic Recommendation Filtering, SERF, Collaborative filterin