ISLF: Interest Shift and Latent Factors Combination Model for Session-based
Recommendation
Abstract
Session-based recommendation is a challenging
problem due to the inherent uncertainty of user
behavior and the limited historical click information. Latent factors and the complex dependencies
within the user’s current session have an important
impact on the user’s main intention, but the existing methods do not explicitly consider this point.
In this paper, we propose a novel model, Interest Shift and Latent Factors Combination Model
(ISLF), which can capture the user’s main intention by taking into account the user’s interest shift
(i.e. long-term and short-term interest) and latent
factors simultaneously. In addition, we experimentally give an explicit explanation of this combination in our ISLF. Our experimental results on three
benchmark datasets show that our model achieves
state-of-the-art performance on all test datasets