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Language independent recommender agent

Part of: Adaptive Learning Agents 2017

Published online by Cambridge University Press:  04 October 2018

Osman Yucel  and
Sandip Sen
Osman Yucel
Affiliation:
Tandy School of Computer Science, The University of Tulsa, 800 S Tucker Dr, Tulsa, OK, USA e-mail: osman-yucel@utulsa.edu, sandip-sen@utulsa.edu
Sandip Sen
Affiliation:
Tandy School of Computer Science, The University of Tulsa, 800 S Tucker Dr, Tulsa, OK, USA e-mail: osman-yucel@utulsa.edu, sandip-sen@utulsa.edu
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Abstract

This paper presents a new ‘Language Independent Recommender Agent’ (LIRA), using information distributed over any text-source pair on the Web about candidate items. While existing review-based recommendation systems learn the features of candidate items and users’ preferences, they do not handle varying perspectives of users on those features. LIRA constructs agents for each user, which run regression algorithms on texts from different sources and builds trust relations. The key advantages of LIRA can be listed as: LIRA does not require reviews from target users, LIRA calculates trust values based on prediction accuracy instead of social connections or rating similarity, LIRA does not require the reviews to come from the same community or peer user group. Since ratings of the reviewers are not necessary for LIRA, we can collect and use reviews from different sources (web pages, professional critiques), as long as we know the corresponding item and source of that text. Since LIRA does not combine text from different sources, texts from different sources are not required to be in the same language. LIRA can utilize text from multiple languages, as long as sources are consistent with their language usage.

Type
Adaptive and Learning Agents
Information
The Knowledge Engineering Review , Volume 33 , 2018 , e15
Copyright
© Cambridge University Press, 2018 

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