Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

Information and Communication Technologies in Tourism

Online vendors employ collaborative filtering algorithms to provide recommendations to their customers so that they can increase their sales and profits. Although recommendation schemes are successful in e-commerce sites, they are vulnerable to shilling or profile injection attacks. On one hand, online shopping sites utilize collaborative filtering schemes to enhance their competitive edge over other companies. On the other hand, malicious users and/or competing vendors might decide to insert fake profiles into the user-item matrices in such a way so that they can affect the predicted ratings on behalf of their advantages. In the past decade, various studies have been conducted to scrutinize different shilling attacks strategies, profile injection attack types, shilling attack detection schemes, robust algorithms proposed to overcome such attacks, and evaluate them with respect to accuracy, cost/benefit, and overall performance. Due to their popularity and importance, we survey about shilling attacks in collaborative filtering algorithms. Giving an overall picture about various shilling attack types by introducing new classification attributes is imperative for further research. Explaining shilling attack detection schemes in detail and robust algorithms proposed so far might open a lead to develop new detection schemes and enhance such robust algorithms further, even propose new ones. Thus, we describe various attack types and introduce new dimensions for attack classification. Detailed description of the proposed detection and robust recommendation algorithms are given. Moreover, we briefly explain evaluation of the proposed schemes. We conclude the paper by discussing various open questions.

Shilling attacks against recommender systems: a comprehensive survey

Providing high quality recommendations is important for e-commerce systems to assist users in making effective selection decisions from a plethora of choices. Collaborative filtering is a widely accepted technique to generate recommendations based on the ratings of like-minded users. However, it suffers from several inherent issues such as data sparsity and cold start. To address these problems, we propose a novel method called ''Merge'' to incorporate social trust information (i.e., trusted neighbors explicitly specified by users) in providing recommendations. Specifically, ratings of a user's trusted neighbors are merged to complement and represent the preferences of the user and to find other users with similar preferences (i.e., similar users). In addition, the quality of merged ratings is measured by the confidence considering the number of ratings and the ratio of conflicts between positive and negative opinions. Further, the rating confidence is incorporated into the computation of user similarity. The prediction for a given item is generated by aggregating the ratings of similar users. Experimental results based on three real-world data sets demonstrate that our method outperforms other counterparts both in terms of accuracy and coverage.

/pdf/merging-trust-in-collaborative-filtering-to-alleviate-data-4gxxqcanll.pdf

Merging trust in collaborative filtering to alleviate data sparsity and cold start

In open and dynamic multiagent systems (MASs), agents often need to rely on resources or services provided by other agents to accomplish their goals. During this process, agents are exposed to the risk of being exploited by others. These risks, if not mitigated, can cause serious breakdowns in the operation of MASs and threaten their long-term wellbeing. To protect agents from the uncertainty in the behavior of their interaction partners, the age-old mechanism of trust between human beings is re-contexted into MASs. The basic idea is to let agents self-police the MAS by rating each other on the basis of their observed behavior and basing future interaction decisions on such information. Over the past decade, a large number of trust management models were proposed. However, there is a lack of research effort in several key areas, which are critical to the success of trust management in MASs where human beings and agents coexist. The purpose of this paper is to give an overview of existing research in trust management in MASs. We analyze existing trust models from a game theoretic perspective to highlight the special implications of including human beings in an MAS, and propose a possible research agenda to advance the state of the art in this field.

http://romisatriawahono.net/lecture/rm/survey/pervasive%20computing/Yu%20-%20Multi-Agent%20Trust%20Management%20-%202013.pdf

A Survey of Multi-Agent Trust Management Systems

During a trip, tourists are mostly dependent on their mobile phone to select their next points of interest (POI). A mobile application that recommends POIs such as tourist attractions or restaurants is based on the user's location data such as check-in history. This article recommends a novel approach to leverage the check-in data captured by location-based social networks (LBSNs) with an aim to improve POI recommendations through personalized explanations. The proposed algorithm generates a user's motivation profile, and its applicability is presented by analyzing a dataset extracted from a popular LBSN. A between-subject experiment (N = 182) is conducted that shows explanations generated using a user's motivation profile increase transparency, which leads to intent to use the LBSN. Perceived usefulness of the LBSN also increases intent to use. The study indicates that when suggesting a POI, recommender system developers include explanations based on user's motivation behavior profile.

Points of Interest Recommendations Based on Check-In Motivations

Adoption of e-government services varies across different categories of services: government to citizen services, utility services, commercial services, informational services, social benefit services and mobile based services. Faced with the constraint of limited resources, policy makers face the challenge of resource allocation among these categories of services. Prioritizing among the different categories of services can help policy makers in resource allocation. This paper attempts to address this challenge by showing the applicability of Granger causality analysis in studying the causation analysis between the categories of egovernment services. We show the applicability of Granger causality analysis in understanding the interdependence between e-government service categories by analyzing real transactions data of six categories of e-government services in India. Our analysis shows that the growth of inquiry services in positively impacting the growth of government to citizen transactions is statistically significant. Besides this, both utility services and mobile based services show significant bi-directional Granger causality with government to citizen services. We also discuss policy implications of our analysis.

/pdf/e-government-services-adoption-and-interdependence-granger-4j1nxe9kv5.pdf

E-government Services Adoption and Interdependence: Granger Causality Analysis

Developing countries, with limited ICT resources available, are heavily dependent upon Telecentres for delivery of e-Government services to citizens, especially in rural areas. As a result, success of e-Governance initiatives in these countries is largely dependent on the financial sustainability of the Telecentres. Technology for delivery of e-Government services is gradually shifting from PC-based model to the mobile-based model. Using exploratory research methodology in context of Common Service Centers in India, this paper examines the impact of this technology shift and aims to understand the issues pertaining to financial sustainability of Telecentres as mobile adoption increases. Our research suggest that it will be difficult for the CSCs to be sustainable in the future and we recommend that government should take steps towards helping the key Telecentres stakeholders develop new capabilities and skills for a new role in the changing environment to retain their relevance and sustainability in the future.

Exploring the Impact of Mobile Technology Adoption on Financial Sustainability of Telecentres: The case of India

The traditional method of recommender systems suffers from the Sparsity problem whereby incomplete dataset results in poor recommendations. Another issue is the drifting preference, i.e. the change of the user's preference with time. In this paper, we propose an algorithm that takes minimal inputs to do away with the Sparsity problem and takes the drift into consideration giving more priority to latest data. The streams of elements are decomposed into the corresponding attributes and are classified in a preferential list with tags as "Sporadic", "New", "Regular", "Old" and "Past" - each category signifying a changing preference over the previous respectively. A repeated occurrence of attribute set of interest implies the user's preference for such attribute(s). The proposed algorithm is based on drifting preference and has been tested with the Yahoo Webscope R4 dataset. Results have shown that our algorithm have shown significant improvements over the comparable "Sliding Window" algorithm.

Sanjog Ray

Papers

Points of Interest Recommendations Based on Check-In Motivations

E-government Services Adoption and Interdependence: Granger Causality Analysis

Exploring the Impact of Mobile Technology Adoption on Financial Sustainability of Telecentres: The case of India

Categorizing user interests in recommender systems

Studies on agrotechniques for reducing irrigation requirement with better utilization of nutrients by irrigated wheat