97 research outputs found
Personalized Web Search Techniques - A Review
Searching is one of the commonly used task on the Internet. Search engines are the basic tool of the internet, from which related information can be collected according to the specified query or keyword given by the user, and are extremely popular for recurrently used sites. With the remarkable development of the World Wide Web (WWW), the information search has grown to be a major business segment of a global, competitive and money-making market. A perfect search engine is the one which should travel through all the web pages inthe WWW and should list the related information based on the given user keyword. In spite of the recent developments on web search technologies, there are still many conditions in which search engine users obtains the non-relevant search results from the search engines. A personalized Web search has various levels of efficiency for different users, queries, and search contexts. Even though personalized search has been a major research area for many years and many personalization approaches have been examined, it is still uncertain whether personalization is always significant on different queries for diverse users and under different search contexts. This paper focusses on the survey of many efficient personalized Web search approaches which were proposed by many authors
Considering correlation retarded growth for personalized recommendation in social tagging
Due to the massive amounts of data, finding social media suited to their need is a challenging issue. To help such users retrieve useful social media content, we propose a new model of personalized recommendation system by using annotating information from relationship among users, tags, and items. However, the frequency of users’ tagging has strong or weak correlation, which affects the dynamic interest mining of users. In this paper, CRGI is proposed to describe the correlation between users and tags or tags and items. Our approach has two phases, in the first phase, we describe the correlation between users, items and tags by CRGI and in the second phase, we build a tag-item weight model and a user-tag preference model on the basis of the first phase. Then we utilize the two models to find the suitable items with the highest scores. The experimental results demonstrate that the item recommendation performance is improved in both the accuracy and the diversity, and validate that the proposed personalized approach is effective for improving the social media recommendation
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
A multi-layer framework for personalized social tag-based applications
Recent years have seen an increasing diffusion of online communities giving their members the ability of specifying and sharing metadata concerning online resources. Such practice, also known as social or collaborative tagging, has the purpose of collecting and sharing opinions about Web resources and simplifying their retrieval. In this paper, we go one step further and show how tags can have more enhanced applications to be exploited for customizing Web content fruition. More precisely, we propose a multi-layer framework where data collected by social tagging communities are complemented with additional services. Such services provide users the ability of expressing their dis/agreement with existing tags, denoting the members they trust based on their characteristics and relationships, or specifying policies on which "quality" assessment of resources should be returned. Besides providing the formal specification of the proposed framework, we illustrate two case studies we have implemented and the experiments we have carried out in order to verify the feasibility of our approach
Modelo para la recuperaciĂłn de informaciĂłn con expansiĂłn de consulta y perfil de preferencia de los usuarios
Comprender la intenciĂłn de bĂşsqueda del usuario permite identificar y extraer los resultados de bĂşsqueda más relevantes y personalizados de la informaciĂłn disponible segĂşn sus necesidades. En el presente artĂculo se plantea un algoritmo para la recuperaciĂłn de informaciĂłn relevante que combina las preferencias del perfil del usuario y la expansiĂłn de consulta para obtener resultados de bĂşsqueda relevantes y personalizados. El proceso de recuperaciĂłn de informaciĂłn se valida mediante las mĂ©tricas de Precision, Recall y Mean Average Precision (MAP) aplicadas a un conjunto de datos que contiene los documentos estandarizados y los perfiles de preferencias. Los resultados permitieron demostrar que el algoritmo mejora el proceso de recuperaciĂłn de informaciĂłn al arrojar documentos con mejor calidad y relevancia segĂşn las necesidades de los usuarios
Recommender systems in industrial contexts
This thesis consists of four parts: - An analysis of the core functions and
the prerequisites for recommender systems in an industrial context: we identify
four core functions for recommendation systems: Help do Decide, Help to
Compare, Help to Explore, Help to Discover. The implementation of these
functions has implications for the choices at the heart of algorithmic
recommender systems. - A state of the art, which deals with the main techniques
used in automated recommendation system: the two most commonly used algorithmic
methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization
methods are detailed. The state of the art presents also purely content-based
methods, hybridization techniques, and the classical performance metrics used
to evaluate the recommender systems. This state of the art then gives an
overview of several systems, both from academia and industry (Amazon, Google
...). - An analysis of the performances and implications of a recommendation
system developed during this thesis: this system, Reperio, is a hybrid
recommender engine using KNN methods. We study the performance of the KNN
methods, including the impact of similarity functions used. Then we study the
performance of the KNN method in critical uses cases in cold start situation. -
A methodology for analyzing the performance of recommender systems in
industrial context: this methodology assesses the added value of algorithmic
strategies and recommendation systems according to its core functions.Comment: version 3.30, May 201
Recherche d'Information Sociale et Recommandation: Etat d'art et travaux futurs
International audienceThe explosion of web 2.0 and social networks has created an enormous and rewarding source of information that has motivated researchers in different fields to exploit it. Our work revolves around the issue of access and identification of social information and their use in building a user profile enriched with a social dimension, and operating in a process of personalization and recommendation. We study several approaches of Social IR (Information Retrieval), distinguished by the type of incorporated social information. We also study various social recommendation approaches classified by the type of recommendation. We then present a study of techniques for modeling the social user profile dimension, followed by a critical discussion. Thus, we propose our social recommendation approach integrating an advanced social user profile model.L’explosion du web 2.0 et des réseaux sociaux a crée une source d’information énorme et enrichissante qui a motivé les chercheurs dans différents domaines à l’exploiter. Notre travail s’articule autour de la problématique d’accès et d’identification des informations sociales et leur exploitation dans la construction d’un profil utilisateur enrichi d’une dimension sociale, et son exploitation dans un processus de personnalisation et de recommandation. Nous étudions différentes approches sociales de RI (Recherche d’Information), distinguées par le type d’informations sociales incorporées. Nous étudions également diverses approches de recommandation sociale classées par le type de recommandation. Nous exposons ensuite une étude des techniques de modélisation de la dimension sociale du profil utilisateur, suivie par une discussion critique. Ainsi, nous présentons notre approche de recommandation sociale proposée intégrant un modèle avancé de profil utilisateur social
Improved collaborative filtering using clustering and association rule mining on implicit data
The recommender systems are recently becoming more significant due to their ability in making decisions on appropriate choices. Collaborative Filtering (CF) is the most successful and most applied technique in the design of a recommender system where items to an active user will be recommended based on the past rating records from like-minded users. Unfortunately, CF may lead to poor recommendation when user ratings on items are very sparse (insufficient number of ratings) in comparison with the huge number of users and items in user-item matrix. In the case of a lack of user rating on items, implicit feedback is used to profile a user’s item preferences. Implicit feedback can indicate users’ preferences by providing more evidences and information through observations made on users’ behaviors. Data mining technique, which is the focus of this research, can predict a user’s future behavior without item evaluation and can too, analyze his preferences. In order to investigate the states of research in CF and implicit feedback, a systematic literature review has been conducted on the published studies related to topic areas in CF and implicit feedback. To investigate users’ activities that influence the recommender system developed based on the CF technique, a critical observation on the public recommendation datasets has been carried out. To overcome data sparsity problem, this research applies users’ implicit interaction records with items to efficiently process massive data by employing association rules mining (Apriori algorithm). It uses item repetition within a transaction as an input for association rules mining, in which can achieve high recommendation accuracy. To do this, a modified preprocessing has been employed to discover similar interest patterns among users. In addition, the clustering technique (Hierarchical clustering) has been used to reduce the size of data and dimensionality of the item space as the performance of association rules mining. Then, similarities between items based on their features have been computed to make recommendations. Experiments have been conducted and the results have been compared with basic CF and other extended version of CF techniques including K-Means Clustering, Hybrid Representation, and Probabilistic Learning by using public dataset, namely, Million Song dataset. The experimental results demonstrate that the proposed technique exhibits improvements of an average of 20% in terms of Precision, Recall and Fmeasure metrics when compared to the basic CF technique. Our technique achieves even better performance (an average of 15% improvement in terms of Precision and Recall metrics) when compared to the other extended version of CF techniques, even when the data is very sparse
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