A survey of recommendation techniques based on offline data processing

Recommendations based on offline data processing has attracted increasing attention from both research communities and IT industries. The recommendation techniques could be used to explore huge volumes of data, identify the items that users probably like, translate the research results into real-world applications and so on. This paper surveys the recent progress in the research of recommendations based on offline data processing, with emphasis on new techniques (such as temporal recommendation, graph-based recommendation and trust-based recommendation), new features (such as serendipitous recommendation) and new research issues (such as tag recommendation and group recommendation). We also provide an extensive review of evaluation measurements, benchmark data sets and available open source tools. Finally, we outline some existing challenges for future research.

Modelling multilevel data in multimedia : A hierarchical factor analysis approach

Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.

Deferentially private tagging recommendation based on topic model

Tagging recommender system allows Internet users to annotate resources with personalized tags and provides users the freedom to obtain recommendations. However, It is usually confronted with serious privacy concerns, because adversaries may re-identify a user and her/his sensitive tags with only a little background information. This paper proposes a privacy preserving tagging release algorithm, PriTop, which is designed to protect users under the notion of differential privacy. The proposed PriTop algorithm includes three privacy preserving operations: Private Topic Model Generation structures the uncontrolled tags, Private Weight Perturbation adds Laplace noise into the weights to hide the numbers of tags; while Private Tag Selection finally finds the most suitable replacement tags for the original tags. We present extensive experimental results on four real world datasets and results suggest the proposed PriTop algorithm can successfully retain the utility of the datasets while preserving privacy. © 2014 Springer International Publishing.

An adaptive information grid architecture for recommendation system

This paper presents an adaptive information grid architecture for recommendation systems, which consists of the features of the recommendation rule and a co-citation algorithm. The algorithm addresses some challenges that are essential for further searching and recommendation algorithms. It does not require users to provide a lot of interactive communication. Furthermore, it supports other queries, such as keyword, URL and document investigations. When the structure is compared to other algorithms, the scalability is noticeably better. The high online performance can be obtained as well as the repository computation, which can achieve a high group-forming accuracy using only a fraction of web pages from a cluster.

A co-recommendation algorithm for web searching

This paper presents an approach called the Co-Recommendation Algorithm, which consists of the features of the recommendation rule and the co-citation algorithm. The algorithm addresses some challenges that are essential for further searching and recommendation algorithms. It does not require users to provide a lot of interactive communication. Furthermore, it supports other queries, such as keyword, URL and document investigations. When the structure is compared to other algorithms, the scalability is noticeably easier. The high online performance can be obtained as well as the repository computation, which can achieve a high group-forming accuracy using only a fraction of Web pages from a cluster.

Enhancing RFID tag resistance against cloning attack

In its current form, RFID system are susceptible to a range of malevolent attacks. With the rich business intelligence that RFID infrastructure could possibly carry, security is of paramount importance. In this paper, we formalise various threat models due tag cloning on the RFID system. We also present a simple but efficient and cost effect technique that strengthens the resistance of RFID tags to cloning attacks. Our techniques can even strengthen tags against cloning in environments with untrusted reading devices.

Preference networks : probabilistic models for recommendation systems

Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Preference Network (PN) that jointly models various types of domain knowledge for the task of recommendation. The PN is a probabilistic model that systematically combines both content-based filtering and collaborative filtering into a single conditional Markov random field. Once estimated, it serves as a probabilistic database that supports various useful queries such as rating prediction and top-N recommendation. To handle the challenging problem of learning large networks of users and items, we employ a simple but effective pseudo-likelihood with regularisation. Experiments on the movie rating data demonstrate the merits of the PN.

A secure tag ownership transfer scheme in a closed loop RFID system

In this paper we propose a novel secure tag ownership transfer scheme for closed loop RFID systems. An important property of our method is that the ownership transfer is guaranteed to be atomic and the scheme is protected against desynchronisation leading to permanent DoS. Further, it is suited to the computational constraints of EPC Class-1 Gen-2 passive RFID tags as they only use the CRC and PRNG functions that passive RFID tags are capable of. We provide a detailed security analysis to show that our scheme satisfies the required security properties of tag anonymity, tag location privacy, forward secrecy, forward untraceability while being resistant to replay, desynchronisation and server impersonation attacks. Performance comparisons show that our scheme is practical and can be implemented on passive low-cost RFID tags.

Impact of personalized recommendation and social comparison on learning behaviours and outcomes

ELearning suffers from the lack of face-to-face interaction and can deprive learners from the benefits of social interaction and comparison. In this paper we present the results of a study conducted for the impact of social comparison. The study was conducted by collecting students’ engagement with an eLearning tool, the attendance, and grades scored by students at specific milestones and presented these metrics to students as feedback using Kiviat charts. The charts were complemented with appropriate recommendations to allow them to adapt their study strategy and behaviour. The study spanned over 4 semesters (2 with and 2 without the Kiviats) and the results were analysed using paired T tests to test the pre and post results on topics covered by the eLearning tool. Survey questionnaires were also administered at the end for qualitative analysis. The results indicated that the Kiviat feedback with recommendation had positive impact on learning outcomes and attitudes.

StenoCipher to provide data confidentiality and tampered data recovery for RFID tag

Radio Frequency Identification (RFID) is an emerging wireless object identification technology with many potential applications such as supply chain management, personnel tracking and healthcare. However, security vulnerabilities of the RFID system have been a serious concern for its wide adoption in many applications. Although much work has been done to provide privacy and anonymity, little focus has been given to ensure RFID data confidentiality, integrity and to address the tampered data recovery problem. To this end, we propose a lightweight stenographic-based approach to ensure RFID data confidentiality and integrity as well as the recovery of tampered RFID data.

Secure RFID tag ownership transfer based on quadratic residues

In this paper, we propose a novel approach to secure ownership transfer in RFID systems based on the quadratic residue property. We present two secure ownership transfer schemes-the closed loop and open loop schemes. An important property of our schemes is that ownership transfer is guaranteed to be atomic. Further, both our schemes are suited to the computational constraints of EPC Class-1 Gen-2 passive RFID tags as they only use operations that such passive RFID tags are capable of. We provide a detailed security analysis to show that our schemes achieve strong privacy and satisfy the required security properties of tag anonymity, tag location privacy, forward secrecy, and forward untraceability. We also show that the schemes are resistant to replay (both passive and algebraic), desynchronization, and server impersonation attacks. Performance comparisons demonstrate that our schemes are practical and can be implemented on low-cost passive RFID tags.

Hybrid approach to ensure data confidentiality and tampered data recovery for RFID tag

Radio Frequency Identification (RFID) is an emerging wireless object identification technology with many potential applications such as supply chain management, personnel tracking and healthcare. However, security vulnerabilities of the RFID system have been a serious concern for its wide adoption in many applications. Although there are lots of work to provide privacy and anonymity, little focus has been given to ensure confidentiality and integrity of RFID tag data. To this end, we propose a lightweight hybrid approach based on stenographic and watermarking to ensure data confidentiality, linkability resistance and integrity on the RFID tags data. The proposed technique is capable of tampered data recovering and restoring for RFID tag. It has been validated and tested on EPC class 1 gen2 tags.

Secure ownership transfer in multi-tag/multi-owner passive RFID systems

In this paper we propose a secure ownership transfer protocol for a multi-tag and multi-owner RFID environment. Most of the existing work in this area do not comply with the EPC Global Class-1 Gen-2 (C1G2) standard since they use expensive hash operations or sophisticated encryption schemes that cannot be implemented on low-cost passive tags that are highly resource constrained. Our work aims to fill this gap by proposing a protocol based on simple XOR and 128-bit Pseudo Random Number Generators (PRNG), operations that can be easily implemented on low-cost passive RFID tags. The protocol thus achieves EPC C1G2 compliance while meeting the security requirements. Also, our protocol provides additional protection using a blind-factor to prevent tracking attacks.

Security protocols for EPC class-1 Gen-2 RFID multi-tag systems

The objective of the research is to develop security protocols for EPC C1G2 RFID Passive Tags in the areas of ownership transfer and grouping proof.