Recommender systems (RS) work effective at alleviating information overload
and matching user interests in various web-scale applications. Most RS retrieve
the user's favorite candidates and then rank them by the rating scores in the
greedy manner. In the permutation prospective, however, current RS come to
reveal the following two limitations: 1) They neglect addressing the
permutation-variant influence within the recommended results; 2) Permutation
consideration extends the latent solution space exponentially, and current RS
lack the ability to evaluate the permutations. Both drive RS away from the
permutation-optimal recommended results and better user experience. To
approximate the permutation-optimal recommended results effectively and
efficiently, we propose a novel permutation-wise framework PRS in the
re-ranking stage of RS, which consists of Permutation-Matching (PMatch) and
Permutation-Ranking (PRank) stages successively. Specifically, the PMatch stage
is designed to obtain the candidate list set, where we propose the FPSA
algorithm to generate multiple candidate lists via the permutation-wise and
goal-oriented beam search algorithm. Afterwards, for the candidate list set,
the PRank stage provides a unified permutation-wise ranking criterion named LR
metric, which is calculated by the rating scores of elaborately designed
permutation-wise model DPWN. Finally, the list with the highest LR score is
recommended to the user. Empirical results show that PRS consistently and
significantly outperforms state-of-the-art methods. Moreover, PRS has achieved
a performance improvement of 11.0% on PV metric and 8.7% on IPV metric after
the successful deployment in one popular recommendation scenario of Taobao
application.

提出了一个新的置换相关的推荐系统架构 PRF，并且通过在淘宝应用程序的实际应用中取得 11.0％的 PV 平均值和 8.7％的 IPV 平均值，性能超过当前最先进的方法。