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After modeling user behavior and item representations, we now move to the Rating Prediction step. At this stage, HiSeR predicts the relevance between a user and each candidate item. To do this, the model combines two representations.
First, we use the user representation, denoted as eue_ueu, which is obtained from the Sequential Encoder and reflects the user¡¯s current preference state. Second, we use the candidate item representation, denoted as vcv_cvc, which is generated by the Hybrid Item Encoder using both the item ID and its associated reviews.
As shown in Equation (11), the prediction score is calculated using the dot product between the user vector and the candidate item vector. This score measures how well the user¡¯s current interests align with the characteristics of the candidate item.
Finally, we use this score for recommendation. A higher prediction score indicates a higher likelihood that the user will interact with the candidate item, and these scores are used to rank items in the recommendation list. |
IEEE/ICACT20260275 Slide.09
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