Neural Re-ranking in Multi-stage Recommender Systems: A Review

Abstract: As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects user experience and satisfaction by rearranging the input ranking lists, and thereby plays a critical role in MRS. With the advances in deep learning, neural re-ranking has become a trending topic and been widely applied in industrial applications. This review aims at integrating re-ranking algorithms into a broader picture, and paving ways for more comprehensive solutions for future research. For this purpose, we first present a taxonomy of current methods on neural re-ranking. Then we give a description of these methods along with the historic development according to their objectives. The network structure, personalization, and complexity are also discussed and compared. Next, we provide benchmarks of the major neural re-ranking models and quantitatively analyze their re-ranking performance. Finally, the review concludes with a discussion on future prospects of this field. A list of papers discussed in this review, the benchmark datasets, our re-ranking library LibRerank, and detailed parameter settings are publicly available at https://github.com/LibRerank-Community/LibRerank.

• The paper reviews state-of-the-art neural re-ranking techniques that refine initial recommendations by reordering candidate lists based on learned relevance.
• It evaluates multi-stage recommender architectures, highlighting trade-offs between recommendation accuracy and computational efficiency.
• The study discusses future directions for integrating deep learning models into recommender pipelines, suggesting avenues for enhanced personalization.

Formal Analysis of the IJCAI-19 Formatting Instructions

The “IJCAI-19 Formatting Instructions” paper provides a thorough guideline for authors who wish to submit manuscripts to the 28th International Joint Conference on Artificial Intelligence (IJCAI-19). The document is a vital resource aimed at ensuring consistency and uniformity across all submissions. It meticulously outlines the technical requirements and stylistic protocols that authors need to adhere to—a crucial aspect for maintaining the professional integrity of the conference proceedings.

Structural Guidelines

Central to the paper is its exhaustive dissection of the document structure. The formatting instructions emphasize an 8.5” x 11” page size, delineating precise margins, column dimensions, and spacing requirements which are contingent on the page number and section type. This precision in layout is essential for the efficient use of space and ensuring the readability of the proceedings. The paper provides guidance on the use of \LaTeX and Microsoft Word, offering templates to facilitate adherence to these standards.

The paper mandates a maximum six-page limit for initial submissions, with an additional page allowed solely for references. It also highlights potential variations in these policies for the camera-ready versions, dictated by different conference tracks. Such specification of submission length constraints serves both as a challenge and an opportunity for authors to distill their research findings into concise, impactful narratives.

Content Presentation

The manuscript stresses the importance of an abstract that provides a precise overview of the paper's scope and significance within a 200-word limit. The guidelines recommend using title case for headings and specify fonts and formatting details for conveying hierarchy and emphasis within the text. This includes comprehensive instructions on section headings and their hierarchical sub-types, down to options for using titled paragraphs within subsections.

Visual coherence is further augmented by explicit instructions for illustrating content. Figures, tables, and algorithms must be integrated at relevant points within the narrative rather than being appended, ensuring that they complement the discussion flow. The inclusion of readable, well-captioned figures is underscored, alongside recommendations for producing clear, monochromatic drawings to prevent quality degradation during reproduction.

Citations and References

The guide delineates citation conventions, underscoring the necessity for clarity and consistency within the text. The format requires citations with authors’ names and publication years, while encouraging precision to avoid ambiguity. Such detailed referencing protocols are crucial for allowing peer verification of sources and intellectual transparency.

Implications for the AI Community

While the document might appear to be a functional guide devoid of experimental findings or theoretical contributions typically expected of conference submissions, its implications for the artificial intelligence community are nonetheless significant. By mandating stringent submission standards, IJCAI-19 seeks to maintain a high quality of academic discourse and presentation, which in turn promotes the reliability and professionalism of AI research outputs.

Future considerations could involve the adaptation of these guidelines to accommodate evolving formatting technologies or publication mediums. Moreover, the potential integration of automated tools to check compliance with formatting standards might facilitate smoother submission processes, thereby freeing researchers to focus more on content quality than on formatting minutiae.

In summary, this paper serves not only as a submission guide but as a cornerstone for upholding the standards of scholarly communication within the AI research community. By enforcing stringent formatting criteria, the IJCAI-19 guidelines help ensure that the dissemination of AI research continues to be conducted with a level of professionalism befitting its rapid advancements and growing impact.