576 research outputs found
Semantic Jira - Semantic Expert Finder in the Bug Tracking Tool Jira
The semantic expert recommender extension for the Jira bug tracking system
semantically searches for similar tickets in Jira and recommends experts and
links to existing organizational (Wiki) knowledge for each ticket. This helps
to avoid redundant work and supports the search and collaboration with experts
in the project management and maintenance phase based on semantically enriched
tickets in Jira.Comment: published in proceedings of the 9th International Workshop on
Semantic Web Enabled Software Engineering (SWESE2013), Berlin, Germany,
December 2-5, 201
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
This paper provides a unified account of two schools of thinking in
information retrieval modelling: the generative retrieval focusing on
predicting relevant documents given a query, and the discriminative retrieval
focusing on predicting relevancy given a query-document pair. We propose a game
theoretical minimax game to iteratively optimise both models. On one hand, the
discriminative model, aiming to mine signals from labelled and unlabelled data,
provides guidance to train the generative model towards fitting the underlying
relevance distribution over documents given the query. On the other hand, the
generative model, acting as an attacker to the current discriminative model,
generates difficult examples for the discriminative model in an adversarial way
by minimising its discrimination objective. With the competition between these
two models, we show that the unified framework takes advantage of both schools
of thinking: (i) the generative model learns to fit the relevance distribution
over documents via the signals from the discriminative model, and (ii) the
discriminative model is able to exploit the unlabelled data selected by the
generative model to achieve a better estimation for document ranking. Our
experimental results have demonstrated significant performance gains as much as
23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of
applications including web search, item recommendation, and question answering.Comment: 12 pages; appendix adde
Developing an Ontological approach to Content-based Recommendation System
During past decade the no of web user increases so rapidly leading to rapid increase in web services which leads to increase the usage data at higher rate. The usage data of these users now amount to be in order of Peta Byte (1015 bytes). In such cases the search space for user‟s queries increases and a user‟s search query may leads to retrieval of irrelevant information. Sometime the search algorithm may become exhaustive. This project is aimed to use User‟s context information to model a framework which can filter the search space and choose some preference based on user‟s context. The project follows a set of processes for profile construction of Users and Items, and determining their similarity and scoring their preference. The projects also compares the effectiveness in predicting User‟s preferences and accuracy in it with various other Collaborative approach such as User-Based model and Item based model to check its performance level and quality of predictions
Enhancing Collaborative Filtering Using Implicit Relations in Data
International audienceThis work presents a Recommender System (RS) that relies on distributed recommendation techniques and implicit relations in data. In order to simplify the experience of users, recommender systems pre-select and filter information in which they may be interested in. Users express their interests in items by giving their opinion (explicit data) and navigating through the web-page (implicit data). The Matrix Fac-torization (MF) recommendation technique analyze this feedback, but it does not take more heterogeneous data into account. In order to improve recommendations, the description of items can be used to increase the relations among data. Our proposal extends MF techniques by adding implicit relations in an independent layer. Indeed, using past preferences, we deeply analyze the implicit interest of users in the attributes of items. By using this, we transform ratings and predictions into " semantic values " , where the term semantic indicates the expansion in the meaning of ratings. The experimentation phase uses MovieLens and IMDb database. We compare our work against a simple Matrix Factorization technique. Results show accurate personalized recommendations. At least but not at last, both recommendation analysis and semantic analysis can be par-allelized, alleviating time processing in large amount of data
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