62 research outputs found
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
Sequential recommendations have made great strides in accurately predicting
the future behavior of users. However, seeking accuracy alone may bring side
effects such as unfair and overspecialized recommendation results. In this
work, we focus on the calibrated recommendations for sequential recommendation,
which is connected to both fairness and diversity. On the one hand, it aims to
provide fairer recommendations whose preference distributions are consistent
with users' historical behaviors. On the other hand, it can improve the
diversity of recommendations to a certain degree. But existing methods for
calibration have mainly relied on the post-processing on the candidate lists,
which require more computation time in generating recommendations. In addition,
they fail to establish the relationship between accuracy and calibration,
leading to the limitation of accuracy. To handle these problems, we propose an
end-to-end framework to provide both accurate and calibrated recommendations
for sequential recommendation. We design an objective function to calibrate the
interests between recommendation lists and historical behaviors. We also
provide distribution modification approaches to improve the diversity and
mitigate the effect of imbalanced interests. In addition, we design a
decoupled-aggregated model to improve the recommendation. The framework assigns
two objectives to two individual sequence encoders, and aggregates the outputs
by extracting useful information. Experiments on benchmark datasets validate
the effectiveness of our proposed model
A comparison of calibrated and intent-aware recommendations
Calibrated and intent-aware recommendation are recent approaches to recommendation that have apparent similarities. Both try, to a certain extent, to cover the user's interests, as revealed by her user profile. In this paper, we compare them in detail. On two datasets, we show the extent to which intent-aware recommendations are calibrated and the extent to which calibrated recommendations are diverse. We consider two ways of defining a user's interests, one based on item features, the other based on subprofiles of the user's profile. We find that defining interests in terms of subprofiles results in highest precision and the best relevance/diversity trade-off. Along the way, we define a new version of calibrated recommendation and three new evaluation metrics
MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels
Multi-Objective Recommender Systems (MORSs) emerged as a paradigm to
guarantee multiple (often conflicting) goals. Besides accuracy, a MORS can
operate at the global level, where additional beyond-accuracy goals are met for
the system as a whole, or at the individual level, meaning that the
recommendations are tailored to the needs of each user. The state-of-the-art
MORSs either operate at the global or individual level, without assuming the
co-existence of the two perspectives. In this study, we show that when global
and individual objectives co-exist, MORSs are not able to meet both types of
goals. To overcome this issue, we present an approach that regulates the
recommendation lists so as to guarantee both global and individual
perspectives, while preserving its effectiveness. Specifically, as individual
perspective, we tackle genre calibration and, as global perspective, provider
fairness. We validate our approach on two real-world datasets, publicly
released with this paper
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