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On cold start for associative tag recommendation
Author(s) -
Martins Eder F.,
Belém Fabiano M.,
Almeida Jussara M.,
Gonçalves Marcos A.
Publication year - 2016
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23353
Subject(s) - computer science , exploit , baseline (sea) , cold start (automotive) , relevance (law) , relevance feedback , set (abstract data type) , focus (optics) , information retrieval , recommender system , reliability (semiconductor) , function (biology) , data mining , machine learning , artificial intelligence , image (mathematics) , image retrieval , programming language , computer security , law , aerospace engineering , oceanography , optics , engineering , biology , power (physics) , quantum mechanics , evolutionary biology , political science , physics , geology
Tag recommendation strategies that exploit term co‐occurrence patterns with tags previously assigned to the target object have consistently produced state‐of‐the‐art results. However, such techniques work only for objects with previously assigned tags. Here we focus on tag recommendation for objects with no tags, a variation of the well‐known \textit{cold start} problem. We start by evaluating state‐of‐the‐art co‐occurrence based methods in cold start. Our results show that the effectiveness of these methods suffers in this situation. Moreover, we show that employing various automatic filtering strategies to generate an initial tag set that enables the use of co‐occurrence patterns produces only marginal improvements. We then propose a new approach that exploits both positive and negative user feedback to iteratively select input tags along with a genetic programming strategy to learn the recommendation function. Our experimental results indicate that extending the methods to include user relevance feedback leads to gains in precision of up to 58% over the best baseline in cold start scenarios and gains of up to 43% over the best baseline in objects that contain some initial tags (i.e., no cold start). We also show that our best relevance‐feedback‐driven strategy performs well even in scenarios that lack user cooperation (i.e., users may refuse to provide feedback) and user reliability (i.e., users may provide the wrong feedback).