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Temporal diversity in recommender systems

Temporal diversity in recommender systems,10.1145/1835449.1835486,Neal Lathia,Stephen Hailes,Licia Capra,Xavier Amatriain

Temporal diversity in recommender systems   (Citations: 1)
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Collaborative Filtering (CF) algorithms, used to build web-based recommender systems, are often evaluated in terms of how accurately they predict user ratings. However, current evaluation techniques disregard the fact that users continue to rate items over time: the temporal characteristics of the system's top-N recommendations are not investigated. In particular, there is no means of measuring the extent that the same items are being recommended to users over and over again. In this work, we show that temporal diversity is an important facet of recommender systems, by showing how CF data changes over time and performing a user survey. We then evaluate three CF algorithms from the point of view of the diversity in the sequence of recommendation lists they produce over time. We examine how a number of characteristics of user rating patterns (including profile size and time between rating) affect diversity. We then propose and evaluate set methods that maximise temporal recommendation diversity without extensively penalising accuracy.
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    • ...The work presented in [11] addresses the temporal diversity of recommender systems...
    • ...The typical time period is at least one week [11]...
    • ...According to [11], the time evolution of L can be captured either by the diversity or the novelty...
    • ...It is interesting to compare the evolution over time of TV and NF (for the latter, see [11])...

    Paolo Cremonesiet al. Controlling Consistency in Top-N Recommender Systems

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