An Academic Search Engine for Personalized Rankings

Rapidly increasing information on the Internet and the World Wide Web can lead to information overload. Search engines become important tools to help WWW users to discover information. Exponential increases in published research papers, academic search engines become indispensable tools to search for papers in their expertise and related fields. In order to improve the quality of search, an academic search engines' capability should be enhanced. This paper proposes a search engine for personalized rankings. In order to evaluate the performance of personalized rankings, thirty-five graduate students from the Department of Web Engineering and Mobile Application Development at Dhurakij Pundit University are participants in the research experiment. Participants are asked to use a prototype of an academic search engine to find and bookmark any research papers according to their interests, which would guarantee that each participants' list of interesting research papers could be recorded. Normalized Discounted Cumulative Gain (NDCG) is used as a metric to determine the performance of the personalized rankings. The experiments suggest that the personalized rankings outperform the original search rankings. Hence, the proposed academic search engine with personalized ranking benefits research paper discovery

Document recommender agent based on hybrid approach

As Internet continues to grow, user tends to rely heavily on search engines. However, these search engines tend to generate a huge number of search results and potentially making it difficult for users to find the most relevant sites. This has resulted in search engines losing their usefulness. These users might be academicians who are searching for relevant academic papers within their interests. The need for a system that can assist in choosing the most relevant papers among the long list of results presented by search engines becomes crucial. In this paper, we propose Document Recommender Agent, that can recommend the most relevant papers based on the academician’s interest. This recommender agent adopts a hybrid recommendation approach. In this paper we also show that recommendation based on the proposed hybrid approach is better that the content-based and the collaborative approaches

Tag-based Paper Retrieval: Minimizing User Effort with Diversity Awareness

As the number of scientific papers getting published is likely to soar, most of modern paper management systems (e.g. ScienceWise, Mendeley, CiteULike) support tag-based retrieval. In that, each paper is associated with a set of \emph{tags}, allowing user to search for relevant papers by formulating tag-based queries against the system. One of the most critical issues in tag-based retrieval is that user often has difficulties in precisely formulating his information need. Addressing this issue, our paper tackles the problem of automatically suggesting new tags for user when he formulates a query. The set of tags are selected in such a way that resolves query ambiguity in two aspects: \emph{informativeness} and \emph{diversity}. While the former reduces user effort in finding the desired papers, the latter enhances the variety of information shown to user. Through studying theoretical properties of this problem, we propose a heuristic-based algorithm with several salient performance guarantees. We also demonstrate the efficiency of our approach through extensive experimentation using real-world datasets

Leveraging social relevance : using social networks to enhance literature access and microblog search

L'objectif principal d'un système de recherche d'information est de sélectionner les documents pertinents qui répondent au besoin en information exprimé par l'utilisateur à travers une requête. Depuis les années 1970-1980, divers modèles théoriques ont été proposés dans ce sens pour représenter les documents et les requêtes d'une part et les apparier d'autre part, indépendamment de tout utilisateur. Plus récemment, l'arrivée du Web 2.0 ou le Web social a remis en cause l'efficacité de ces modèles du fait qu'ils ignorent l'environnement dans lequel l'information se situe. En effet, l'utilisateur n'est plus un simple consommateur de l'information mais il participe également à sa production. Pour accélérer la production de l'information et améliorer la qualité de son travail, l'utilisateur échange de l'information avec son voisinage social dont il partage les mêmes centres d'intérêt. Il préfère généralement obtenir l'information d'un contact direct plutôt qu'à partir d'une source anonyme. Ainsi, l'utilisateur, influencé par son environnement socio-cultuel, donne autant d'importance à la proximité sociale de la ressource d'information autant qu'à la similarité des documents à sa requête. Dans le but de répondre à ces nouvelles attentes, la recherche d'information s'oriente vers l'implication de l'utilisateur et de sa composante sociale dans le processus de la recherche. Ainsi, le nouvel enjeu de la recherche d'information est de modéliser la pertinence compte tenu de la position sociale et de l'influence de sa communauté. Le second enjeu est d'apprendre à produire un ordre de pertinence qui traduise le mieux possible l'importance et l'autorité sociale. C'est dans ce cadre précis, que s'inscrit notre travail. Notre objectif est d'estimer une pertinence sociale en intégrant d'une part les caractéristiques sociales des ressources et d'autre part les mesures de pertinence basées sur les principes de la recherche d'information classique. Nous proposons dans cette thèse d'intégrer le réseau social d'information dans le processus de recherche d'information afin d'utiliser les relations sociales entre les acteurs sociaux comme une source d'évidence pour mesurer la pertinence d'un document en réponse à une requête. Deux modèles de recherche d'information sociale ont été proposés à des cadres applicatifs différents : la recherche d'information bibliographique et la recherche d'information dans les microblogs. Les importantes contributions de chaque modèle sont détaillées dans la suite. Un modèle social pour la recherche d'information bibliographique. Nous avons proposé un modèle générique de la recherche d'information sociale, déployé particulièrement pour l'accès aux ressources bibliographiques. Ce modèle représente les publications scientifiques au sein d'réseau social et évalue leur importance selon la position des auteurs dans le réseau. Comparativement aux approches précédentes, ce modèle intègre des nouvelles entités sociales représentées par les annotateurs et les annotations sociales. En plus des liens de coauteur, ce modèle exploite deux autres types de relations sociales : la citation et l'annotation sociale. Enfin, nous proposons de pondérer ces relations en tenant compte de la position des auteurs dans le réseau social et de leurs mutuelles collaborations. Un modèle social pour la recherche d'information dans les microblogs.} Nous avons proposé un modèle pour la recherche de tweets qui évalue la qualité des tweets selon deux contextes: le contexte social et le contexte temporel. Considérant cela, la qualité d'un tweet est estimé par l'importance sociale du blogueur correspondant. L'importance du blogueur est calculée par l'application de l'algorithme PageRank sur le réseau d'influence sociale. Dans ce même objectif, la qualité d'un tweet est évaluée selon sa date de publication. Les tweets soumis dans les périodes d'activité d'un terme de la requête sont alors caractérisés par une plus grande importance. Enfin, nous proposons d'intégrer l'importance sociale du blogueur et la magnitude temporelle avec les autres facteurs de pertinence en utilisant un modèle Bayésien.An information retrieval system aims at selecting relevant documents that meet user's information needs expressed with a textual query. For the years 1970-1980, various theoretical models have been proposed in this direction to represent, on the one hand, documents and queries and on the other hand to match information needs independently of the user. More recently, the arrival of Web 2.0, known also as the social Web, has questioned the effectiveness of these models since they ignore the environment in which the information is located. In fact, the user is no longer a simple consumer of information but also involved in its production. To accelerate the production of information and improve the quality of their work, users tend to exchange documents with their social neighborhood that shares the same interests. It is commonly preferred to obtain information from a direct contact rather than from an anonymous source. Thus, the user, under the influenced of his social environment, gives as much importance to the social prominence of the information as the textual similarity of documents at the query. In order to meet these new prospects, information retrieval is moving towards novel user centric approaches that take into account the social context within the retrieval process. Thus, the new challenge of an information retrieval system is to model the relevance with regards to the social position and the influence of individuals in their community. The second challenge is produce an accurate ranking of relevance that reflects as closely as possible the importance and the social authority of information producers. It is in this specific context that fits our work. Our goal is to estimate the social relevance of documents by integrating the social characteristics of resources as well as relevance metrics as defined in classical information retrieval field. We propose in this work to integrate the social information network in the retrieval process and exploit the social relations between social actors as a source of evidence to measure the relevance of a document in response to a query. Two social information retrieval models have been proposed in different application frameworks: literature access and microblog retrieval. The main contributions of each model are detailed in the following. A social information model for flexible literature access. We proposed a generic social information retrieval model for literature access. This model represents scientific papers within a social network and evaluates their importance according to the position of respective authors in the network. Compared to previous approaches, this model incorporates new social entities represented by annotators and social annotations (tags). In addition to co-authorships, this model includes two other types of social relationships: citation and social annotation. Finally, we propose to weight these relationships according to the position of authors in the social network and their mutual collaborations. A social model for information retrieval for microblog search. We proposed a microblog retrieval model that evaluates the quality of tweets in two contexts: the social context and temporal context. The quality of a tweet is estimated by the social importance of the corresponding blogger. In particular, blogger's importance is calculated by the applying PageRank algorithm on the network of social influence. With the same aim, the quality of a tweet is evaluated according to its date of publication. Tweets submitted in periods of activity of query terms are then characterized by a greater importance. Finally, we propose to integrate the social importance of blogger and the temporal magnitude tweets as well as other relevance factors using a Bayesian network model