Visible to the public Biblio

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2021-04-08
Yang, Z., Sun, Q., Zhang, Y., Zhu, L., Ji, W..  2020.  Inference of Suspicious Co-Visitation and Co-Rating Behaviors and Abnormality Forensics for Recommender Systems. IEEE Transactions on Information Forensics and Security. 15:2766—2781.
The pervasiveness of personalized collaborative recommender systems has shown the powerful capability in a wide range of E-commerce services such as Amazon, TripAdvisor, Yelp, etc. However, fundamental vulnerabilities of collaborative recommender systems leave space for malicious users to affect the recommendation results as the attackers desire. A vast majority of existing detection methods assume certain properties of malicious attacks are given in advance. In reality, improving the detection performance is usually constrained due to the challenging issues: (a) various types of malicious attacks coexist, (b) limited representations of malicious attack behaviors, and (c) practical evidences for exploring and spotting anomalies on real-world data are scarce. In this paper, we investigate a unified detection framework in an eye for an eye manner without being bothered by the details of the attacks. Firstly, co-visitation and co-rating graphs are constructed using association rules. Then, attribute representations of nodes are empirically developed from the perspectives of linkage pattern, structure-based property and inherent association of nodes. Finally, both attribute information and connective coherence of graph are combined in order to infer suspicious nodes. Extensive experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed detection approach compared with competing benchmarks. Additionally, abnormality forensics metrics including distribution of rating intention, time aggregation of suspicious ratings, degree distributions before as well as after removing suspicious nodes and time series analysis of historical ratings, are provided so as to discover interesting findings such as suspicious nodes (items or ratings) on real-world data.
2021-03-29
Pranav, E., Kamal, S., Chandran, C. Satheesh, Supriya, M. H..  2020.  Facial Emotion Recognition Using Deep Convolutional Neural Network. 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). :317—320.

The rapid growth of artificial intelligence has contributed a lot to the technology world. As the traditional algorithms failed to meet the human needs in real time, Machine learning and deep learning algorithms have gained great success in different applications such as classification systems, recommendation systems, pattern recognition etc. Emotion plays a vital role in determining the thoughts, behaviour and feeling of a human. An emotion recognition system can be built by utilizing the benefits of deep learning and different applications such as feedback analysis, face unlocking etc. can be implemented with good accuracy. The main focus of this work is to create a Deep Convolutional Neural Network (DCNN) model that classifies 5 different human facial emotions. The model is trained, tested and validated using the manually collected image dataset.

2021-01-11
Wang, J., Wang, A..  2020.  An Improved Collaborative Filtering Recommendation Algorithm Based on Differential Privacy. 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS). :310–315.
In this paper, differential privacy protection method is applied to matrix factorization method that used to solve the recommendation problem. For centralized recommendation scenarios, a collaborative filtering recommendation model based on matrix factorization is established, and a matrix factorization mechanism satisfying ε-differential privacy is proposed. Firstly, the potential characteristic matrix of users and projects is constructed. Secondly, noise is added to the matrix by the method of target disturbance, which satisfies the differential privacy constraint, then the noise matrix factorization model is obtained. The parameters of the model are obtained by the stochastic gradient descent algorithm. Finally, the differential privacy matrix factorization model is used for score prediction. The effectiveness of the algorithm is evaluated on the public datasets including Movielens and Netflix. The experimental results show that compared with the existing typical recommendation methods, the new matrix factorization method with privacy protection can recommend within a certain range of recommendation accuracy loss while protecting the users' privacy information.
2020-12-01
Sun, P., Yin, S., Man, W., Tao, T..  2018.  Research of Personalized Recommendation Algorithm Based on Trust and User's Interest. 2018 International Conference on Robots Intelligent System (ICRIS). :153—156.

Most traditional recommendation algorithms only consider the binary relationship between users and projects, these can basically be converted into score prediction problems. But most of these algorithms ignore the users's interests, potential work factors or the other social factors of the recommending products. In this paper, based on the existing trustworthyness model and similarity measure, we puts forward the concept of trust similarity and design a joint interest-content recommendation framework to suggest users which videos to watch in the online video site. In this framework, we first analyze the user's viewing history records, tags and establish the user's interest characteristic vector. Then, based on the updated vector, users should be clustered by sparse subspace clust algorithm, which can improve the efficiency of the algorithm. We certainly improve the calculation of similarity to help users find better neighbors. Finally we conduct experiments using real traces from Tencent Weibo and Youku to verify our method and evaluate its performance. The results demonstrate the effectiveness of our approach and show that our approach can substantially improve the recommendation accuracy.

Herse, S., Vitale, J., Tonkin, M., Ebrahimian, D., Ojha, S., Johnston, B., Judge, W., Williams, M..  2018.  Do You Trust Me, Blindly? Factors Influencing Trust Towards a Robot Recommender System 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). :7—14.

When robots and human users collaborate, trust is essential for user acceptance and engagement. In this paper, we investigated two factors thought to influence user trust towards a robot: preference elicitation (a combination of user involvement and explanation) and embodiment. We set our experiment in the application domain of a restaurant recommender system, assessing trust via user decision making and perceived source credibility. Previous research in this area uses simulated environments and recommender systems that present the user with the best choice from a pool of options. This experiment builds on past work in two ways: first, we strengthened the ecological validity of our experimental paradigm by incorporating perceived risk during decision making; and second, we used a system that recommends a nonoptimal choice to the user. While no effect of embodiment is found for trust, the inclusion of preference elicitation features significantly increases user trust towards the robot recommender system. These findings have implications for marketing and health promotion in relation to Human-Robot Interaction and call for further investigation into the development and maintenance of trust between robot and user.

2020-11-23
Li, W., Zhu, H., Zhou, X., Shimizu, S., Xin, M., Jin, Q..  2018.  A Novel Personalized Recommendation Algorithm Based on Trust Relevancy Degree. 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). :418–422.
The rapid development of the Internet and ecommerce has brought a lot of convenience to people's life. Personalized recommendation technology provides users with services that they may be interested according to users' information such as personal characteristics and historical behaviors. The research of personalized recommendation has been a hot point of data mining and social networks. In this paper, we focus on resolving the problem of data sparsity based on users' rating data and social network information, introduce a set of new measures for social trust and propose a novel personalized recommendation algorithm based on matrix factorization combining trust relevancy. Our experiments were performed on the Dianping datasets. The results show that our algorithm outperforms traditional approaches in terms of accuracy and stability.
2020-10-12
Puspitaningrum, Diyah, Fernando, Julio, Afriando, Edo, Utama, Ferzha Putra, Rahmadini, Rina, Pinata, Y..  2019.  Finding Local Experts for Dynamic Recommendations Using Lazy Random Walk. 2019 7th International Conference on Cyber and IT Service Management (CITSM). 7:1–6.
Statistics based privacy-aware recommender systems make suggestions more powerful by extracting knowledge from the log of social contacts interactions, but unfortunately, they are static - moreover, advice from local experts effective in finding specific business categories in a particular area. We propose a dynamic recommender algorithm based on a lazy random walk that recommends top-rank shopping places to potentially interested visitors. We consider local authority and topical authority. The algorithm tested on FourSquare shopping data sets of 5 cities in Indonesia with k-steps=5,7,9 (lazy) random walks and compared the results with other state-of-the-art ranking techniques. The results show that it can reach high score precisions (0.5, 0.37, and 0.26 respectively on p@1, p@3, and p@5 for k=5). The algorithm also shows scalability concerning execution time. The advantage of dynamicity is the database used to power the recommender system; no need to be very frequently updated to produce a good recommendation.
Luma, Artan, Abazi, Blerton, Aliu, Azir.  2019.  An approach to Privacy on Recommended Systems. 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). :1–5.
Recommended systems are very popular nowadays. They are used online to help a user get the desired product quickly. Recommended Systems are found on almost every website, especially big companies such as Facebook, eBay, Amazon, NetFlix, and others. In specific cases, these systems help the user find a book, movie, article, product of his or her preference, and are also used on social networks to meet friends who share similar interests in different fields. These companies use referral systems because they bring amazing benefits in a very fast time. To generate more accurate recommendations, recommended systems are based on the user's personal information, eg: different ratings, history observation, personal profiles, etc. Use of these systems is very necessary but the way this information is received, and the privacy of this information is almost constantly ignored. Many users are unaware of how their information is received and how it is used. This paper will discuss how recommended systems work in different online companies and how safe they are to use without compromising their privacy. Given the widespread use of these systems, an important issue has arisen regarding user privacy and security. Collecting personal information from recommended systems increases the risk of unwanted exposure to that information. As a result of this paper, the reader will be aware of the functioning of Recommended systems, the way they receive and use their information, and will also discuss privacy protection techniques against Recommended systems.
2020-10-05
Parvina, Hashem, Moradi, Parham, Esmaeilib, Shahrokh, Jalilic, Mahdi.  2018.  An Efficient Recommender System by Integrating Non-Negative Matrix Factorization With Trust and Distrust Relationships. 2018 IEEE Data Science Workshop (DSW). :135—139.

Matrix factorization (MF) has been proved to be an effective approach to build a successful recommender system. However, most current MF-based recommenders cannot obtain high prediction accuracy due to the sparseness of user-item matrix. Moreover, these methods suffer from the scalability issues when applying on large-scale real-world tasks. To tackle these issues, in this paper a social regularization method called TrustRSNMF is proposed that incorporates the social trust information of users in nonnegative matrix factorization framework. The proposed method integrates trust statements along with user-item ratings as an additional information source into the recommendation model to deal with the data sparsity and cold-start issues. In order to evaluate the effectiveness of the proposed method, a number of experiments are performed on two real-world datasets. The obtained results demonstrate significant improvements of the proposed method compared to state-of-the-art recommendation methods.

Kang, Anqi.  2018.  Collaborative Filtering Algorithm Based on Trust and Information Entropy. 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). 3:262—266.

In order to improve the accuracy of similarity, an improved collaborative filtering algorithm based on trust and information entropy is proposed in this paper. Firstly, the direct trust between the users is determined by the user's rating to explore the potential trust relationship of the users. The time decay function is introduced to realize the dynamic portrayal of the user's interest decays over time. Secondly, the direct trust and the indirect trust are combined to obtain the overall trust which is weighted with the Pearson similarity to obtain the trust similarity. Then, the information entropy theory is introduced to calculate the similarity based on weighted information entropy. At last, the trust similarity and the similarity based on weighted information entropy are weighted to obtain the similarity combing trust and information entropy which is used to predicted the rating of the target user and create the recommendation. The simulation shows that the improved algorithm has a higher accuracy of recommendation and can provide more accurate and reliable recommendation service.

2020-07-13
Li, Tao, Ren, Yongzhen, Ren, Yongjun, Wang, Lina, Wang, Lingyun, Wang, Lei.  2019.  NMF-Based Privacy-Preserving Collaborative Filtering on Cloud Computing. 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). :476–481.
The security of user personal information on cloud computing is an important issue for the recommendation system. In order to provide high quality recommendation services, privacy of user is often obtained by untrusted recommendation systems. At the same time, malicious attacks often use the recommendation results to try to guess the private data of user. This paper proposes a hybrid algorithm based on NMF and random perturbation technology, which implements the recommendation system and solves the protection problem of user privacy data in the recommendation process on cloud computing. Compared with the privacy protection algorithm of SVD, the elements of the matrix after the decomposition of the new algorithm are non-negative elements, avoiding the meaninglessness of negative numbers in the matrix formed by texts, images, etc., and it has a good explanation for the local characteristics of things. Experiments show that the new algorithm can produce recommendation results with certain accuracy under the premise of protecting users' personal privacy on cloud computing.
2020-06-12
Ay, Betül, Aydın, Galip, Koyun, Zeynep, Demir, Mehmet.  2019.  A Visual Similarity Recommendation System using Generative Adversarial Networks. 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). :44—48.

The goal of content-based recommendation system is to retrieve and rank the list of items that are closest to the query item. Today, almost every e-commerce platform has a recommendation system strategy for products that customers can decide to buy. In this paper we describe our work on creating a Generative Adversarial Network based image retrieval system for e-commerce platforms to retrieve best similar images for a given product image specifically for shoes. We compare state-of-the-art solutions and provide results for the proposed deep learning network on a standard data set.

2020-05-18
Liu, Xueqing.  2018.  Assisting the Development of Secure Mobile Apps with Natural Language Processing. 2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). :279–280.
With the rapid growth of mobile devices and mobile apps, mobile has surpassed desktop and now has the largest worldwide market share [1]. While such growth brings in more opportunities, it also poses new challenges in security. Among the challenges, user privacy protection has drawn tremendous attention in recent years, especially after the Facebook-Cambridge Analytica data scandal in April 2018 [2].
2020-04-13
Horne, Benjamin D., Gruppi, Mauricio, Adali, Sibel.  2019.  Trustworthy Misinformation Mitigation with Soft Information Nudging. 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :245–254.

Research in combating misinformation reports many negative results: facts may not change minds, especially if they come from sources that are not trusted. Individuals can disregard and justify lies told by trusted sources. This problem is made even worse by social recommendation algorithms which help amplify conspiracy theories and information confirming one's own biases due to companies' efforts to optimize for clicks and watch time over individuals' own values and public good. As a result, more nuanced voices and facts are drowned out by a continuous erosion of trust in better information sources. Most misinformation mitigation techniques assume that discrediting, filtering, or demoting low veracity information will help news consumers make better information decisions. However, these negative results indicate that some news consumers, particularly extreme or conspiracy news consumers will not be helped. We argue that, given this background, technology solutions to combating misinformation should not simply seek facts or discredit bad news sources, but instead use more subtle nudges towards better information consumption. Repeated exposure to such nudges can help promote trust in better information sources and also improve societal outcomes in the long run. In this article, we will talk about technological solutions that can help us in developing such an approach, and introduce one such model called Trust Nudging.

2020-03-23
Hu, Rui, Guo, Yuanxiong, Pan, Miao, Gong, Yanmin.  2019.  Targeted Poisoning Attacks on Social Recommender Systems. 2019 IEEE Global Communications Conference (GLOBECOM). :1–6.
With the popularity of online social networks, social recommendations that rely on one’s social connections to make personalized recommendations have become possible. This introduces vulnerabilities for an adversarial party to compromise the recommendations for users by utilizing their social connections. In this paper, we propose the targeted poisoning attack on the factorization-based social recommender system in which the attacker aims to promote an item to a group of target users by injecting fake ratings and social connections. We formulate the optimal poisoning attack as a bi-level program and develop an efficient algorithm to find the optimal attacking strategy. We then evaluate the proposed attacking strategy on real-world dataset and demonstrate that the social recommender system is sensitive to the targeted poisoning attack. We find that users in the social recommender system can be attacked even if they do not have direct social connections with the attacker.
Nakayama, Johannes, Plettenberg, Nils, Halbach, Patrick, Burbach, Laura, Ziefle, Martina, Calero Valdez, André.  2019.  Trust in Cyber Security Recommendations. 2019 IEEE International Professional Communication Conference (ProComm). :48–55.
Over the last two decades, the Internet has established itself as part of everyday life. With the recent invention of Social Media, the advent of the Internet of Things as well as trends like "bring your own device" (BYOD), the needs for connectivity rise exponentially and so does the need for proper cyber security. However, human factors research of cyber security in private contexts comprises only a small fraction of the research in the field. In this study, we investigated adoption behaviours and trust in cyber security in private contexts by measuring - among other trust measures - disposition to trust and providing five cyber security scenarios. In each, a person/agent recommends the use of a cyber security tool. Trust is then measured regarding the recommending agent. We compare personal, expert, institutional, and magazine recommendations along with manufacturer information in an exploratory study of sixty participants. We found that personal, expert and institutional recommendations were trusted significantly more than manufacturer information and magazine reports. The highest trust scores were produced by the expert and the personal recommendation scenarios. We argue that technical and professional communicators should aim for cyber security knowledge permeation through personal relations, educating people with high technology self-efficacy beliefs who then disperse the acquired knowledge.
Kim, MinJu, Dey, Sangeeta, Lee, Seok-Won.  2019.  Ontology-Driven Security Requirements Recommendation for APT Attack. 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW). :150–156.
Advanced Persistent Threat (APT) is one of the cyber threats that continuously attack specific targets exfiltrate information or destroy the system [1]. Because the attackers use various tools and methods according to the target, it is difficult to describe APT attack in a single pattern. Therefore, APT attacks are difficult to defend against with general countermeasures. In these days, systems consist of various components and related stakeholders, which makes it difficult to consider all the security concerns. In this paper, we propose an ontology knowledge base and its design process to recommend security requirements based on APT attack cases and system domain knowledge. The proposed knowledge base is divided into three parts; APT ontology, general security knowledge ontology, and domain-specific knowledge ontology. Each ontology can help to understand the security concerns in their knowledge. While integrating three ontologies into the problem domain ontology, the appropriate security requirements can be derived with the security requirements recommendation process. The proposed knowledge base and process can help to derive the security requirements while considering both real attacks and systems.
Rustgi, Pulkit, Fung, Carol.  2019.  Demo: DroidNet - An Android Permission Control Recommendation System Based on Crowdsourcing. 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). :737–738.
Mobile and web application security, particularly the areas of data privacy, has raised much concerns from the public in recent years. Most applications, or apps for short, are installed without disclosing full information to users and clearly stating what the application has access to, which often raises concern when users become aware of unnecessary information being collected. Unfortunately, most users have little to no technical expertise in regards to what permissions should be turned on and can only rely on their intuition and past experiences to make relatively uninformed decisions. To solve this problem, we developed DroidNet, which is a crowd-sourced Android recommendation tool and framework. DroidNet alleviates privacy concerns and presents users with high confidence permission control recommendations based on the decision from expert users who are using the same apps. This paper explains the general framework, principles, and model behind DroidNet while also providing an experimental setup design which shows the effectiveness and necessity for such a tool.
Tu, Qingqing, Jing, Yulin, Zhu, Weiwei.  2019.  Research on Privacy Security Risk Evaluation of Intelligent Recommendation Mobile Applications Based on a Hierarchical Risk Factor Set. 2019 4th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). :638–6384.

Intelligent recommendation applications based on data mining have appeared as prospective solution for consumer's demand recognition in large-scale data, and it has contained a great deal of consumer data, which become the most valuable wealth of application providers. However, the increasing threat to consumer privacy security in intelligent recommendation mobile application (IR App) makes it necessary to have a risk evaluation to narrow the gap between consumers' need for convenience with efficiency and need for privacy security. For the previous risk evaluation researches mainly focus on the network security or information security for a single work, few of which consider the whole data lifecycle oriented privacy security risk evaluation, especially for IR App. In this paper, we analyze the IR App's features based on the survey on both algorithm research and market prospect, then provide a hierarchical factor set based privacy security risk evaluation method, which includes whole data lifecycle factors in different layers.

Xu, Yilin, Ge, Weimin, Li, Xiaohong, Feng, Zhiyong, Xie, Xiaofei, Bai, Yude.  2019.  A Co-Occurrence Recommendation Model of Software Security Requirement. 2019 International Symposium on Theoretical Aspects of Software Engineering (TASE). :41–48.
To guarantee the quality of software, specifying security requirements (SRs) is essential for developing systems, especially for security-critical software systems. However, using security threat to determine detailed SR is quite difficult according to Common Criteria (CC), which is too confusing and technical for non-security specialists. In this paper, we propose a Co-occurrence Recommend Model (CoRM) to automatically recommend software SRs. In this model, the security threats of product are extracted from security target documents of software, in which the related security requirements are tagged. In order to establish relationships between software security threat and security requirement, semantic similarities between different security threat is calculated by Skip-thoughts Model. To evaluate our CoRM model, over 1000 security target documents of 9 types software products are exploited. The results suggest that building a CoRM model via semantic similarity is feasible and reliable.
Karlsson, Linus, Paladi, Nicolae.  2019.  Privacy-Enabled Recommendations for Software Vulnerabilities. 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :564–571.
New software vulnerabilities are published daily. Prioritizing vulnerabilities according to their relevance to the collection of software an organization uses is a costly and slow process. While recommender systems were earlier proposed to address this issue, they ignore the security of the vulnerability prioritization data. As a result, a malicious operator or a third party adversary can collect vulnerability prioritization data to identify the security assets in the enterprise deployments of client organizations. To address this, we propose a solution that leverages isolated execution to protect the privacy of vulnerability profiles without compromising data integrity. To validate an implementation of the proposed solution we integrated it with an existing recommender system for software vulnerabilities. The evaluation of our implementation shows that the proposed solution can effectively complement existing recommender systems for software vulnerabilities.
Unnikrishnan, Grieshma, Mathew, Deepa, Jose, Bijoy A., Arvind, Raju.  2019.  Hybrid Route Recommender System for Smarter Logistics. 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). :239–244.
The condition of road surface has a significant role in land transportation. Due to poor road conditions, the logistics and supply chain industry face a drastic loss in their business. Unmaintained roads can cause damage to goods and accidents. The existing routing techniques do not consider factors like shock, temperature and tilt of goods etc. but these factors have to be considered for the logistics and supply chain industry. This paper proposes a recommender system which target management of goods in logistics. A 3 axis accelerometer is used to measure the road surface conditions. The pothole location is obtained using Global Positioning System (GPS). Using these details a hybrid recommender system is built. Hybrid recommender system combines multiple recommendation techniques to develop an effective recommender system. Here content-based and collaborative-based techniques is combined to build a hybrid recommender system. One of the popular Multiple Criteria Decision Making (MCDM) method, The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used for content based filtering and normalised Euclidean distance and KNN algorithm is used for collaborative filtering. The best route recommended by the system will be displayed to the user using a map application.
Bansal, Saumya, Baliyan, Niyati.  2019.  Evaluation of Collaborative Filtering Based Recommender Systems against Segment-Based Shilling Attacks. 2019 International Conference on Computing, Power and Communication Technologies (GUCON). :110–114.
Collaborative filtering (CF) is a successful and hence most widely used technique for recommender systems. However, it is vulnerable to shilling attack due to its open nature, which results in generating biased or false recommendations for users. In literature, segment attack (push attack) has been widely studied and investigated while rare studies have been performed on nuke attack, to the best of our knowledge. Further, the robustness of binary collaborative filtering and hybrid approach has not been investigated against segment-focused attack. In this paper, from the perspective of robustness, binary collaborative filtering, hybrid approach, stand-alone rating user-based, and stand-alone rating item- based recommendation have been evaluated against segment attack on a large dataset (100K ratings) which is found to be more successful as it attacks target set of items. With an aim to find an approach which reflects a higher accuracy in recommending items and is less vulnerable to segment-based attack, the possibility of any relationship between accuracy and vulnerability of six CF approaches were studied. Such an approach needs to be re-examined by the researchers marking the future of recommender system (RS). Experimental results show negligible positive correlation between accuracy and vulnerability of techniques. Publicly available dataset namely MovieLens was used for conducting experiments. Robustness and accuracy of CF techniques were calculated using prediction shift and F-measure, respectively.
Arul, Tolga, Anagnostopoulos, Nikolaos Athanasios, Katzenbeisser, Stefan.  2019.  Privacy Usability of IPTV Recommender Systems. 2019 IEEE International Conference on Consumer Electronics (ICCE). :1–2.
IPTV is capable of providing recommendations for upcoming TV programs based on consumer feedback. With the increasing popularity and performance of recommender systems, risks of user privacy breach emerge. Although several works about privacy-preserving designs of recommender systems exist in the literature, a detailed analysis of the current state-of-the-art regarding privacy as well as an investigation of the usability aspects of such systems, so far, have not received consideration. In this paper, we survey current approaches for recommender systems by studying their privacy and usability properties in the context of IPTV.
2019-12-09
Yang, Chao, Chen, Xinghe, Song, Tingting, Jiang, Bin, Liu, Qin.  2018.  A Hybrid Recommendation Algorithm Based on Heuristic Similarity and Trust Measure. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :1413–1418.
In this paper, we propose a hybrid collaborative filtering recommendation algorithm based on heuristic similarity and trust measure, in order to alleviate the problem of data sparsity, cold start and trust measure. Firstly, a new similarity measure is implemented by weighted fusion of multiple similarity influence factors obtained from the rating matrix, so that the similarity measure becomes more accurate. Then, a user trust relationship computing model is implemented by constructing the user's trust network based on the trust propagation theory. On this basis, a SIMT collaborative filtering algorithm is designed which integrates trust and similarity instead of the similarity in traditional collaborative filtering algorithm. Further, an improved K nearest neighbor recommendation based on clustering algorithm is implemented for generation of a better recommendation list. Finally, a comparative experiment on FilmTrust dataset shows that the proposed algorithm has improved the quality and accuracy of recommendation, thus overcome the problem of data sparsity, cold start and trust measure to a certain extent.