Authors:
Ashok Veilumuthu
and
Parthasarathy Ramachandran
Affiliation:
Indian Institute of Science, India
Keyword(s):
Ranking Models, Feedback Aggregation, Implicit Feedbacks, Click Sequence, Partial Ordering.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
The current approaches to information retrieval from the search engine depends heavily on the web linkage structure which is a form of relevance judgment by the page authors. However, to overcome spamming attempts and language semantics, it is important to also incorporate the user feedback on the documents’ relevance for a particular query. Since users can be hardly motivated to give explicit/direct feedback on search quality, it becomes necessary to consider implicit feedback that can be collected from search engine logs. Though there are number of implicit feedback measures proposed to improve the search quality, there is no standard methodology proposed yet to aggregate those implicit feedbacks meaningfully to get a final ranking of he documents. In this article, we propose an extension to the distance based ranking model to aggregate different implicit feedbacks based on their expertise in ranking the documents. The proposed approach has been tested on two implicit feedbacks, na
mely click sequence and time spent in reading a document from the actual log data of AlltheWeb.com. The results were found to be convincing and indicative of the possibility of expertise based fusion of implicit feedbacks to arrive at a single ranking of documents for the given query.
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