Revisiting the Tag Relevance Prediction Problem

Denis Kotkov, Alexandr Maslov, Mats Neovius

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

Abstrakti

Traditionally, recommender systems provide a list of suggestions to a user based on past interactions with items of this user. These recommendations are usually based on user preferences for items and generated with a delay. Critiquing recommender systems allow users to provide immediate feedback to recommendations with tags and receive a new set of recommendations in response. However, these systems often require rich item descriptions that contain relevance scores indicating the strength, with which a tag applies to an item. For example, this relevance score could indicate how violent the movie "The Godfather" is on a scale from 0 to 1. Retrieving these data is a very demanding process, as it requires users to explicitly indicate the degree to which a tag applies to an item. This process can be improved with machine learning methods that predict tag relevance. In this paper, we explore the dataset from a different study, where the authors collected relevance scores on movie-tag pairs. In particular, we define the tag relevance prediction problem, explore the inconsistency of relevance scores provided by users as a challenge of this problem and present a method, which outperforms the state-of-the-art method for predicting tag relevance. We found a moderate inconsistency of user relevance scores. We also found that users tend to disagree more on subjective tags, such as "good acting", "bad plot" or "quotable" than on objective tags, such as "animation", "cars" or "wedding", but the disagreement of users regarding objective tags is also moderate.
Alkuperäiskielienglanti
OtsikkoSIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
Sivumäärä5
KustantajaAssociation for Computing Machinery
Julkaisupäivä11 heinäk. 2021
Sivut1768–1772
ISBN (elektroninen)9781450380379
DOI - pysyväislinkit
TilaJulkaistu - 11 heinäk. 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational ACM SIGIR conference on research and development in Information Retrieval - Montréal, Kanada
Kesto: 11 heinäk. 202116 heinäk. 2021
Konferenssinumero: 44
https://sigir.org/sigir2021/

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