Рекомендательные Системы

task ID: recommendationsystems

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Publication: TIPSTER (2012)

DOI: nan

AbstractThis paper proposes a method to construct an evaluation dataset from microblogs for the development of recommendation systems. We extract the relationships among three main entities in a recommendation event, …...

13739

Distributed Representations for Building Profiles of Users and Items from Text Reviews

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Publication: TIPSTER (2016)

DOI: nan

AbstractIn this paper, we propose an approach to learn distributed representations of users and items from text comments for recommendation systems. Traditional recommendation algorithms, e.g. collaborative filtering and matrix completion, …...

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RankDCG: Rank-Ordering Evaluation Measure

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AbstractRanking is used for a wide array of problems, most notably information retrieval (search). Kendall’s τ, Average Precision, and nDCG are a few popular approaches to the evaluation of ranking. …...

15076

A Comparison of Explicit and Implicit Proactive Dialogue Strategies for Conversational Recommendation

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractRecommendation systems aim at facilitating information retrieval for users by taking into account their preferences. Based on previous user behaviour, such a system suggests items or provides information that a …...

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Point-of-Interest Type Inference from Social Media Text

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Publication: TIPSTER (2020)

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AbstractPhysical places help shape how we perceive the experiences we have there. We study the relationship between social media text and the type of the place from where it was …...

269

Humor@IITK at SemEval-2021 Task 7: Large Language Models for Quantifying Humor and Offensiveness

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractHumor and Offense are highly subjective due to multiple word senses, cultural knowledge, and pragmatic competence. Hence, accurately detecting humorous and offensive texts has several compelling use cases in Recommendation …...

25735