A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search (2405.15521v1)
Abstract: Re-ranking is a process of rearranging ranking list to more effectively meet user demands by accounting for the interrelationships between items. Existing methods predominantly enhance the precision of search results, often at the expense of diversity, leading to outcomes that may not fulfill the varied needs of users. Conversely, methods designed to promote diversity might compromise the precision of the results, failing to satisfy the users' requirements for accuracy. To alleviate the above problems, this paper proposes a Preference-oriented Diversity Model Based on Mutual-information (PODM-MI), which consider both accuracy and diversity in the re-ranking process. Specifically, PODM-MI adopts Multidimensional Gaussian distributions based on variational inference to capture users' diversity preferences with uncertainty. Then we maximize the mutual information between the diversity preferences of the users and the candidate items using the maximum variational inference lower bound to enhance their correlations. Subsequently, we derive a utility matrix based on the correlations, enabling the adaptive ranking of items in line with user preferences and establishing a balance between the aforementioned objectives. Experimental results on real-world online e-commerce systems demonstrate the significant improvements of PODM-MI, and we have successfully deployed PODM-MI on an e-commerce search platform.
- Learning a deep listwise context model for ranking refinement. In The 41st international ACM SIGIR conference on research & development in information retrieval. 135–144.
- Seq2slate: Re-ranking and slate optimization with rnns. arXiv preprint arXiv:1810.02019 (2018).
- Jaime Carbonell and Jade Goldstein. 1998. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. 335–336.
- Fast greedy map inference for determinantal point process to improve recommendation diversity. Advances in Neural Information Processing Systems 31 (2018).
- End-to-End User Behavior Retrieval in Click-Through Rate Prediction Model. arXiv preprint arXiv:2108.04468 (2021).
- Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction. arXiv preprint arXiv:2209.12212 (2022).
- Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems. 7–10.
- EdgeRec: recommender system on edge in Mobile Taobao. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2477–2484.
- Practical diversified recommendations on youtube with determinantal point processes. In Proceedings of the 8th ACM Conference on Recommender systems. ACM, 281–284.
- DeepFM: a factorization-machine based neural network for CTR prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 1725–1731.
- Sliding spectrum decomposition for diversified recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3041–3049.
- xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1754–1763.
- Feature-aware diversified re-ranking with disentangled representations for relevant recommendation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3327–3335.
- Validation Set Evaluation can be Wrong: An Evaluator-Generator Approach for Maximizing Online Performance of Ranking in E-commerce. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Xi’an, China, July 25-30, 2020. ACM, 1171–1180. https://doi.org/10.1145/3397271.3401093
- Personalized re-ranking for recommendation. In Proceedings of the 13th ACM conference on recommender systems. 3–11.
- Learning representations by back-propagating errors. nature 323, 6088 (1986), 533–536.
- Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, 11 (2008).
- Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 5941–5948.
- Globally optimized mutual influence aware ranking in e-commerce search. arXiv preprint arXiv:1805.08524 (2018).