  Personalized recommender systems fulfill the daily demands of customers and
boost online businesses. The goal is to learn a policy that can generate a list
of items that matches the user's demand or interest. While most existing
methods learn a pointwise scoring model that predicts the ranking score of each
individual item, recent research shows that the listwise approach can further
improve the recommendation quality by modeling the intra-list correlations of
items that are exposed together. This has motivated the recent list reranking
and generative recommendation approaches that optimize the overall utility of
the entire list. However, it is challenging to explore the combinatorial space
of list actions and existing methods that use cross-entropy loss may suffer
from low diversity issues. In this work, we aim to learn a policy that can
generate sufficiently diverse item lists for users while maintaining high
recommendation quality. The proposed solution, GFN4Rec, is a generative method
that takes the insight of the flow network to ensure the alignment between list
generation probability and its reward. The key advantages of our solution are
the log scale reward matching loss that intrinsically improves the generation
diversity and the autoregressive item selection model that captures the item
mutual influences while capturing future reward of the list. As validation of
our method's effectiveness and its superior diversity during active
exploration, we conduct experiments on simulated online environments as well as
an offline evaluation framework for two real-world datasets.

Information Retrieval