IIM Indore seeks to be a contextually-relevant business school with world-class academic standards that develops socially-conscious managers, leaders and entrepreneurs. IIM Indore is committed to: Excellence in management education, research, and training, Use of contemporary participant-centric pedagogies and teaching methods, A presence in emerging segments of management education.

IIM Indore

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.

World University root node

Information Systems

Tourism Analysis

Points of interest recommendations based on check-in motivations

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.

ACM International Conference Proceeding Series

Exploring the impact of mobile technology adoption on financial sustainability of telecentres: The case of India

Governments across the world have invested in e‑governance projects as they help deliver citizen services efficiently and economically. Given that these projects are public‑funded, they are regularly evaluated to measure the benefits accrued. Existing research on the subject has used impact assessment frameworks with defined dimensions and sub‑dimensions for e‑governance project evaluation. Current research literature in the area of assessment frameworks projects shows that the important dimension of sustainability is either ignored or not defined properly. This study addresses this gap by proposing a set of dimensions and sub‑dimensions that can be used in an e‑governance project to measure its impact on sustainability. The applicability of our proposed framework was validated using a case study approach. The framework was applied on the Common Service Center project in India. The findings reveal that the telecenter project is lacking in different aspects of sustainability as sustainability aspects were not considered at project conceptualization stage. Technological sustainability is the only aspect where the CSC project has performed well. This study would help both policymakers and practitioners in measuring the impact of e‑governance projects on sustainability.

The Electronic Journal of Information Systems Evaluation

Defining Dimensions for Assessing Sustainability in e-Governance Projects

The purpose of this study is to search for the potential applications of the recommender system in the event of a disaster. Furthermore, the present paper evaluates the existing technological capital of nations and its suitability for the deployment of a responsive recommender system. The result of the study suggests that the uninterrupted Internet network is an essential requirement for recommender system. In most of the developing nations, this system is either unavailable or becomes non-usable in the event of a disaster. Therefore, continuous Wi-Fi is the most desired requirement to deploy any recommender system to mitigate the effect of a catastrophe. Facebook's new solar-powered Internet drone is the solution for it. Towards the end, the paper presents a decision matrix for the deployment of suggested Wi-Fi system.

International Journal of Innovation, Management and Technology

Disasters and Recommender System: Setting the Research Agenda for Developing Nations

Adoption of e-government services varies across the different categories of services: government to citizen services, utility services, commercial services, informational services, social benefit services and mobile based services. Faced with constraint of resources, policy makers devising strategies for better adoption of these services face the challenge of prioritizing among these categories of services. This paper attempts to address this challenge by showing the applicability of Granger causality analysis in studying the causation analysis between the categories of e-government services. We prove the applicability of Granger causality analysis in understanding the independence 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.

Journal of International Technology and Information Management

E-government Services Adoption and Interdependence: Granger Causality Analysis

Most existing context aware recommender systems primarily use a combination of ratings data, content data like features or attributes of the product or service, context data like location or time and social network data. In this paper, we propose a novel approach for refining the recommendations made by location-aware recommender systems based on user motivations for checking in at locations in location based social networks. Based on a classification that classifies user’s motivation for checking in at a Point Of Interest into seven categories we propose an approach that will help refine recommendations in a way which can be better explained to the user. We also show the applicability of our approach by analyzing a dataset extracted from Foursquare.

CEUR Workshop Proceedings

A motivation-aware approach for point of interest recommendations

Personalisation of product and services is one of the most important aspects in e-commerce and web-based companies. Recommender system is the technology that helps build personalisation features. One promising and emerging area in the field of recommender systems is explanations. Explanation is the reasoning behind the information presented about a recommendation and plays an important role in helping the user evaluate the recommendation received on websites related to e-commerce, social networking, search engines, etc. In this paper, we present an overview of this emerging concept of explanation which is important in present day context as an aid for overcoming information overload. Present status of research in this field is discussed on the three important areas-objectives and evaluation of performance of explanations, effect of explanation interfaces on the users and the growing influence of explanations in social networking sites. We conclude the paper with discussions on areas of future research in this upcoming field which can be of academic interest to the researchers and of practical utility to the system designers of recommender systems for increasing its effectiveness.

International Journal of Business Information Systems

Explanations in recommender systems: An overview

Trust-ware recommender systems provide the features of personalized product and service recommendations in web based social networks by using the trust connections existing between users and preferences data available for each user. One of the main sources of user preferences data are the tags that users apply to different items. Encouraging users to apply more tags is one of the challenges faced by most social network sites. In this paper we purpose an approach to identify influential taggers in a trust based social network so that efforts to encourage tagging can be achieved by designing incentives for motivating the influential taggers to apply more tags. In our proposed approach, for every user his tagging influencer is that user in his personal network who has influenced his tagging behavior the most. We define an active user tagging actions has been influenced by a user in his personal network only when the active user tags an item after his influencer has tagged it. The influential taggers in the overall social network are those who have the influenced the maximum number of users in the network. We analyze the real life dataset of Last.fm to show that our approach is different from the current approach of defining those users who have tagged the maximum number of items as the influential users. We also discuss the implications of using our approach.

Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012

Identifying Influential Taggers in Trust-Aware Recommender Systems

One aspect of research in the area of bootstrapping of cold start users in trust-aware recommender systems is on guiding new users find trusted users in the trust network. In this paper we examine the question “Which type of users do cold start users actually trust?” We analyze the users trusted by cold start users in the Epinions dataset (Massa &amp; Avesani, 2006a). We examine the set of trusted users on the basis of four critical parameters: number of outgoing links, number of incoming links, number of items rated and a hybrid parameter defined as preference score. We also experimentally evaluate the impact of the four parameters on prediction accuracy. Our analysis shows that cold start users will more likely trust a user with higher number of incoming links as a trusted user even though trusting users with high preference score would result in better prediction accuracy.

Communications of the IIMA

Trusting the trusted: an empirical analysis

In management education programmes today, students face a difficult time in choosing electives as the number of electives available are many. As the range and diversity of different elective courses available for selection have increased, course recommendation systems that help students in making choices about courses have become more relevant. In this paper we extend the concept of collaborative filtering approach to develop a course recommendation system. The proposed approach provides student an accurate prediction of the grade they may get if they choose a particular course, which will be helpful when they decide on selecting elective courses, as grade is an important parameter for a student while deciding on an elective course. We experimentally evaluate the collaborative filtering approach on a real life data set and show that the proposed system is effective in terms of accuracy.

Communications in Computer and Information Science

A collaborative filtering based approach for recommending elective courses

LAP LAMBERT Academic Publishing GmbH &amp; Co

Arsenic toxicity of estuarine mudcrab

Trust-aware recommender systems are intelligent technology applications that make use of trust information and user personal data in social networks to provide personalized recommendations. Earlier research in trust-aware systems have shown that the ability of trust-based systems to make accurate predictions coupled with their robustness from shilling attacks make them a better alternative than traditional recommender systems. In this paper we propose an approach for improving accuracy of predictions in trust-aware recommender systems. In our approach, we first reconstruct the trust network. Trust network is reconstructed by removing trust links between users having correlation coefficient below a specified threshold value. For prediction calculation we compare three different approaches based on trust and correlation. We show through experiments on real life Epinions data set that our proposed approach of reconstructing the trust network gives substantially better prediction accuracy than the original approach of using all trust statements in the network. extcopyright 2010 IEEE.

2010 43rd Hawaii International Conference on System Sciences

Improving Prediction Accuracy in Trust-Aware Recommender Systems

Recommender systems are one of the most popular systems that help in personalization of ecommerce sites and collaborative web sites. Tags are textual description given to resources by users and are prevalent in most web 2.0 websites as they help in organization and retrieval of resources. In this paper we present an overview of the way tags are used in recommender systems and also on the aspect of recommendation of tags for items. This paper surveys the work done on two aspects of recommendations based on tags (a) Recommending items to users and (b) Recommending tags for items. This paper also discusses possible extensions that can improve recommendation capabilities using tags and make better tags recommendation for items.

WORKING PAPER

Use of tags in recommender systems: a survey

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.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Categorizing user interests in recommender systems

For cold start users, trust-aware recommender systems have been shown to be more useful in providing better quality recommendations than traditional systems. By exploiting the concept of trust propagation over the trust network, trust-aware systems are able to make large number of accurate recommendations by utilizing the few trust statements given by the new user. In this paper, we propose intelligent strategies based on efficient heuristics, and examine existing strategies on the basis of which prospective trusted users are selected to be presented before a cold start user. Through experimental evaluation we show that selecting prospective trusted users based on our proposed efficient heuristics i.e. hybrid parameter preference score gives the most accurate recommendations. We also propose a clustering approach to select n users for the final list from the set of prospective users selected by a strategy. Furthermore, our paper also describes a new metric entropy0 to reduce the effort required by a cold start user to evaluate prospective trusted users.

Scopus

Designing accurate trust-aware recommender systems for cold start users

In recent years recommender systems have become a ubiquitous feature in e-commerce sites. However, the open nature of recommender systems makes them vulnerable to shilling attacks from malicious users. Such attacks may lead to erosion of user trust in the objectivity and accuracy of the system. One critical area of research in security of recommender systems is the study of attack models. In this paper, we propose an approach for creating attack models. Our paper explores the importance of target item and filler items in mounting effective shilling attacks. Our attack strategies are based on intelligent selection of filler items. Filler items are selected on the basis of the target item rating distribution. We propose filler item strategies for both all-user attacks and in-segment attacks. We show through experiments that our attack strategies are the most effective attack strategies against both user-based and item-based collaborative filtering systems. © 2009 IEEE.

2009 42nd Hawaii International Conference on System Sciences

Filler item strategies for shilling attacks against recommender systems

One area of research which has recently gained importance is the security of recommender systems. Malicious users may influence the recommender system by inserting biased data into the system. Such attacks may lead to erosion of user trust in the objectivity and accuracy of the system. In this paper, we propose a new approach for creating attack strategies. Our paper explores the importance of target item and filler items in mounting effective shilling attacks. Unlike previous approaches, we propose strategies built specifically for user based and item based collaborative filtering systems. Our attack strategies are based on intelligent selection of filler items. Filler items are selected on the basis of the target item rating distribution. We show through experiments that our strategies are effective against both user based and item based collaborative filtering systems. Our approach is shown to provide substantial improvement in attack effectiveness over existing attack models. extcopyright Springer-Verlag Berlin Heidelberg 2009.

Privacy, Security, and Trust in KDD Lecture Notes in Computer Science

Strategies for Effective Shilling Attacks against Recommender Systems

Recommender systems have become a crucial tool for electronic commerce. These systems help in improving customer satisfaction by providing personalized services. Much of the research in recommender systems is done in solving the top-N recommendation problem where the objective is to identify a set of N items the user will most likely prefer. In this paper, we propose a weighted class based hybrid algorithm to solve this problem. Our algorithm combines the strength of both content based and collaborative filtering approaches. In this method, firstly, classes are defined by applying content based and collaborative filtering techniques on ratings data. Subsequently, user's existing profile of items is examined to determine the weightage of each class. Items are then recommended to the user on the basis of weightage attached to each class. We show through experimental results that our algorithm consistently outperforms existing algorithms when applied on MovieLens data set.