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As future work, we plan to analyze the proposed set of feature extraction techniques with other recommender algorithms, including those specific for the item recommendation task and optimization of personalized rankings, such as the BPR (Bayesian Personalized Ranking) [31], CliMF (Collaborative Less-is-More Filtering) [32], and TFMAP (Tensor Factorization for MAP Maximization) [33].
This makes our recommendation task difficult.
Then, the game props recommendation task can be cast into an MIML prediction problem.
Following this update rule, the MIML approach will minimize the ranking errors on the recommendation task.
In [66], authors propose to use the recommendation task for evaluating purposes.
However, most existing trust-based approaches are designed for the recommendation task of rating prediction.
Open image in new window Fig. 2 Multi-instance multi-label representation for game prop recommendation task.
In game prop recommendation task, as time goes by, players will realize that some props are essential for their game characters, and become interested in buying these props.
We consider only distance-2 neighbors because in the link recommendation task we will restrict our prediction to the closest non-connected pairs for computational efficiency reasons.
LWMF(_u) does the recommendation task based on users, so it can be inferred that selecting points based on users are more reasonable than the other two methods.
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