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User Profile based Information Retrieval Incorporated with Reinforcement Learning |
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PP: 1473-1478 |
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doi:10.18576/amis/110526
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Author(s) |
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S. Subitha,
S. Sujatha,
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Abstract |
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| Information retrieval is a complex process that involves understanding the user’s requirements to provide appropriate and
relevant results. This involves combined working of several techniques such as contextual analysis, correlation analysis, sentiment
analysis and a good understanding of the user’s profile. This paper presents an effective relevance feedback based information retrieval
model that aids in effective retrieval and organization of results such that information relevant to the users are given high priority.
The user’s profile is constructed and reinforced with their queries and selection responses. This is iteratively performed such that the
user’s profile gets strengthened with better and more appropriate rules. Result organization is performed based on the significance
levels, sentiment and user’s preferences. Experiments on STS Gold Sentiment Corpus indicate effective predictions when compared
with recent models. |
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