TME-PSR improves sequential recommendation accuracy and explanation quality by personalizing temporal rhythms, fine-grained interests, and recommendation-explanation alignment using a dual-view time encoder, multihead LRU, and dual-branch mutual information weighting.
A survey on feature weighting based k-means algorithms.Journal of Classification, 33(2):210–242
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TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation
TME-PSR improves sequential recommendation accuracy and explanation quality by personalizing temporal rhythms, fine-grained interests, and recommendation-explanation alignment using a dual-view time encoder, multihead LRU, and dual-branch mutual information weighting.