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

Implementation of an efficient Fuzzy Logic based Information Retrieval System

This paper exemplifies the implementation of an efficient Information Retrieval (IR) System to compute the similarity between a dataset and a query using Fuzzy Logic. TREC dataset has been used for the same purpose. The dataset is parsed to generate keywords index which is used for the similarity comparison with the user query. Each query is assigned a score value based on its fuzzy similarity with the index keywords. The relevant documents are retrieved based on the score value. The performance and accuracy of the proposed fuzzy similarity model is compared with Cosine similarity model using Precision-Recall curves. The results prove the dominance of Fuzzy Similarity based IR system.Comment: arXiv admin note: substantial text overlap with http://ntz-develop.blogspot.in/ , http://www.micsymposium.org/mics2012/submissions/mics2012_submission_8.pdf , http://www.slideshare.net/JeffreyStricklandPhD/predictive-modeling-and-analytics-selectchapters-41304405 by other author

Personalized information retrieval based on context and ontological knowledge

The article has been accepted for publication and appeared in a revised form, subsequent to peer review and/or editorial input by Cambridge University PressExtended papers from C&O-2006, the second International Workshop on Contexts and Ontologies, Theory, Practice and Applications1 collocated with the seventeenth European Conference on Artificial Intelligence (ECAI)Context modeling has been long acknowledged as a key aspect in a wide variety of problem domains. In this paper we focus on the combination of contextualization and personalization methods to improve the performance of personalized information retrieval. The key aspects in our proposed approach are a) the explicit distinction between historic user context and live user context, b) the use of ontology-driven representations of the domain of discourse, as a common, enriched representational ground for content meaning, user interests, and contextual conditions, enabling the definition of effective means to relate the three of them, and c) the introduction of fuzzy representations as an instrument to properly handle the uncertainty and imprecision involved in the automatic interpretation of meanings, user attention, and user wishes. Based on a formal grounding at the representational level, we propose methods for the automatic extraction of persistent semantic user preferences, and live, ad-hoc user interests, which are combined in order to improve the accuracy and reliability of personalization for retrieval.This research was partially supported by the European Commission under contracts FP6-001765 aceMedia and FP6-027685 MESH. The expressed content is the view of the authors but not necessarily the view of the aceMedia or MESH projects as a whole

Recommendation technique-based government-to-business personalized e-services

One of the new directions in current e-government development is to provide personalized online services to citizens and businesses. Recommendation techniques can bring a possible solution for this issue. This study proposes a hybrid recommendation approach to provide personalized government to business (G2B) e-services. The approach integrates fuzzy sets-based semantic similarity and traditional item-based collaborative filtering methods to improve recommendation accuracy. A recommender system named Intelligent Business Partner Locator (IBPL) is designed to apply the proposed recommendation approach for supporting government agencies to recommend business partners. ©2009 IEEE

Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering

User information needs vary significantly across different tasks, and therefore their queries will also differ considerably in their expressiveness and semantics. Many studies have been proposed to model such query diversity by obtaining query types and building query-dependent ranking models. These studies typically require either a labeled query dataset or clicks from multiple users aggregated over the same document. These techniques, however, are not applicable when manual query labeling is not viable, and aggregated clicks are unavailable due to the private nature of the document collection, e.g., in email search scenarios. In this paper, we study how to obtain query type in an unsupervised fashion and how to incorporate this information into query-dependent ranking models. We first develop a hierarchical clustering algorithm based on truncated SVD and varimax rotation to obtain coarse-to-fine query types. Then, we study three query-dependent ranking models, including two neural models that leverage query type information as additional features, and one novel multi-task neural model that views query type as the label for the auxiliary query cluster prediction task. This multi-task model is trained to simultaneously rank documents and predict query types. Our experiments on tens of millions of real-world email search queries demonstrate that the proposed multi-task model can significantly outperform the baseline neural ranking models, which either do not incorporate query type information or just simply feed query type as an additional feature.Comment: CIKM 201

Dynamic adaptation of user profiles in recommender systems

In a period of time in which the content available through the Internet increases exponentially and is more easily accessible every day, techniques for aiding the selection and extraction of important and personalised information are of vital importance. Recommender Systems (RS) appear as a tool to help the user in a decision making process by evaluating a set of objects or alternatives and aiding the user at choosing which one/s of them suits better his/her interests or preferences. Those preferences need to be accurate enough to produce adequate recommendations and should be updated if the user changes his/her likes or if they are incorrect or incomplete. In this work an adequate model for managing user preferences in a multi-attribute (numerical and categorical) environment is presented to aid at providing recommendations in those kinds of contexts. The evaluation process of the recommender system designed is supported by a new aggregation operator (Unbalanced LOWA) that enables the combination of the information that defines an alternative into a single value, which then is used to rank the whole set of alternatives. After the recommendation has been made, learning processes have been designed to evaluate the user interaction with the system to find out, in a dynamic and unsupervised way, if the user profile in which the recommendation process relies on needs to be updated with new preferences. The work detailed in this document also includes extensive evaluation and testing of all the elements that take part in the recommendation and learning processes

User Preference Web Search -- Experiments with a System Connecting Web and User

We present models, methods, implementations and experiments with a system enabling personalized web search for many users with different preferences. The system consists of a web information extraction part, a text search engine, a middleware supporting top-k answers and a user interface for querying and evaluation of search results. We integrate several tools (implementing our models and methods) into one framework connecting user with the web. The model represents user preferences with fuzzy sets and fuzzy logic, here understood as a scoring describing user satisfaction. This model can be acquired with explicit or implicit methods. Model-theoretic semantics is based on fuzzy description logic f-EL. User preference learning is based on our model of fuzzy inductive logic programming. Our system works both for English and Slovak resources. The primary application domain are job offers and job search, however we show extension to mutual investment funds search and a possibility of extension into other application domains. Our top-k search is optimized with own heuristics and repository with special indexes. Our model was experimentally implemented, the integration was tested and is web accessible. We focus on experiments with several users and measure their satisfaction according to correlation coefficients

DeepSQLi: Deep Semantic Learning for Testing SQL Injection

Security is unarguably the most serious concern for Web applications, to which SQL injection (SQLi) attack is one of the most devastating attacks. Automatically testing SQLi vulnerabilities is of ultimate importance, yet is unfortunately far from trivial to implement. This is because the existence of a huge, or potentially infinite, number of variants and semantic possibilities of SQL leading to SQLi attacks on various Web applications. In this paper, we propose a deep natural language processing based tool, dubbed DeepSQLi, to generate test cases for detecting SQLi vulnerabilities. Through adopting deep learning based neural language model and sequence of words prediction, DeepSQLi is equipped with the ability to learn the semantic knowledge embedded in SQLi attacks, allowing it to translate user inputs (or a test case) into a new test case, which is semantically related and potentially more sophisticated. Experiments are conducted to compare DeepSQLi with SQLmap, a state-of-the-art SQLi testing automation tool, on six real-world Web applications that are of different scales, characteristics and domains. Empirical results demonstrate the effectiveness and the remarkable superiority of DeepSQLi over SQLmap, such that more SQLi vulnerabilities can be identified by using a less number of test cases, whilst running much faster

Metamodeling approach to preference management in the semantic Web

2008 AAAI Workshop; Chicago, IL; United States; 13 July 2008 through 14 July 2008Preference is a superiority state to determine the preferable or the superior of one entity, property or constraint to another from a specified selection set. Preference issue is heavily studied in Semantic Web research area. The existing preference management approaches only consider the importance of concepts for capturing users' interests. This paper presents a metamodeling approach to preference management. Preference meta model consists of concepts and semantic relations to represent users' interests. Users may have the same type preferences in different domains. Thus, metamodeling must be used to define similar preferences for interoperability in different domains. In this paper, preference meta model defines a general storage structure to manage different types of preferences for personalized applications. Copyright © 2008, Association for the Advancement of Artificial Intelligence

The role of human factors in stereotyping behavior and perception of digital library users: A robust clustering approach

To deliver effective personalization for digital library users, it is necessary to identify which human factors are most relevant in determining the behavior and perception of these users. This paper examines three key human factors: cognitive styles, levels of expertise and gender differences, and utilizes three individual clustering techniques: k-means, hierarchical clustering and fuzzy clustering to understand user behavior and perception. Moreover, robust clustering, capable of correcting the bias of individual clustering techniques, is used to obtain a deeper understanding. The robust clustering approach produced results that highlighted the relevance of cognitive style for user behavior, i.e., cognitive style dominates and justifies each of the robust clusters created. We also found that perception was mainly determined by the level of expertise of a user. We conclude that robust clustering is an effective technique to analyze user behavior and perception