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2018 Volume 33
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ORIGINAL RESEARCH   Open Access    

Language independent recommender agent

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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.
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  • Cite this article

    Osman Yucel, Sandip Sen. 2018. Language independent recommender agent. The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000218
    Osman Yucel, Sandip Sen. 2018. Language independent recommender agent. The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000218
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ORIGINAL RESEARCH   Open Access    

Language independent recommender agent

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  • Published online: 04 October 2018
The Knowledge Engineering Review  33,  2018, 33(1)  |  Cite this article

Abstract: 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.

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    • The problem of not being able to provide recommendations because of the lack of preference information about the user.

    • Hello in Turkish.

    • This means that LIRA implementations should be distributed and not susceptible to the bottlenecks and single-point-of-failure issues plaguing centralized approaches.

    • This 75–25% split is made on the training set. Since the whole dataset was split as 80% training set and 20% test set, the ratios on the whole split becomes 60% model building set, 20% weight calculation set, and 20% test set.

    • It should be noted that 100 user experiment is conducted to show that accuracy of LIRA does not get affected by fewer number of users. Since available data are a subset of data used in All ratings experiment, which shows the difference is significant, significance values are not necessary for 100 user experiment.

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    Osman Yucel, Sandip Sen. 2018. Language independent recommender agent. The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000218
    Osman Yucel, Sandip Sen. 2018. Language independent recommender agent. The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000218
    shu

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