Enhancing CTR Prediction in Recommendation Domain with Search Query Representation (2410.21487v1)

Abstract: Many platforms, such as e-commerce websites, offer both search and recommendation services simultaneously to better meet users' diverse needs. Recommendation services suggest items based on user preferences, while search services allow users to search for items before providing recommendations. Since users and items are often shared between the search and recommendation domains, there is a valuable opportunity to enhance the recommendation domain by leveraging user preferences extracted from the search domain. Existing approaches either overlook the shift in user intention between these domains or fail to capture the significant impact of learning from users' search queries on understanding their interests. In this paper, we propose a framework that learns from user search query embeddings within the context of user preferences in the recommendation domain. Specifically, user search query sequences from the search domain are used to predict the items users will click at the next time point in the recommendation domain. Additionally, the relationship between queries and items is explored through contrastive learning. To address issues of data sparsity, the diffusion model is incorporated to infer positive items the user will select after searching with certain queries in a denoising manner, which is particularly effective in preventing false positives. Effectively extracting this information, the queries are integrated into click-through rate prediction in the recommendation domain. Experimental analysis demonstrates that our model outperforms state-of-the-art models in the recommendation domain.

• The paper introduces QueryRec, a novel framework that integrates search query representation for enhanced CTR prediction in recommendation systems.
• It employs next-item prediction with self-attention and contrastive learning augmented by diffusion modeling to capture implicit user preferences.
• Experimental results on benchmark datasets demonstrate improved AUC scores over state-of-the-art methods, highlighting its practical scalability.

Enhancing CTR Prediction in Recommendation Systems with Search Query Representation

In the paper "Enhancing CTR Prediction in Recommendation Domain with Search Query Representation," the authors propose a sophisticated approach to improve click-through rate (CTR) predictions in recommendation systems by utilizing user search queries effectively. This paper focuses on the inherent potential in leveraging shared data between search and recommendation services, which are frequently present on platforms such as e-commerce websites, to refine recommendations based on user preferences inferred from their search history.

The primary novelty of this work lies in the integration of search queries, typically reflecting explicit user interests, into the recommendation engines. Existing methods often either underutilize this valuable user input or ignore the changes in user preferences between interacting with search features and recommendation suggestions. The proposed framework seeks to bridge this gap by employing a combination of next-item prediction and contrastive learning, enhanced by diffusion modeling, to derive meaningful insights about user preferences from search query logs.

Framework Overview

At the core of the proposed solution is a framework named QueryRec, designed to utilize search query representation to enhance CTR predictions in the recommendation domain. The framework comprises several key components:

1. Next-Item Prediction: To align search queries with user preferences in the recommendation domain, a next-item prediction module is introduced. This module processes the sequence of a user's search queries to predict the next item the user is likely to click within the recommendation domain. A self-attention mechanism is utilized to aggregate query embeddings, allowing the framework to adaptively align user interests as expressed through searches with subsequent item clicks in recommendations.
2. Contrastive Learning with Diffusion Augmentation: Recognizing that many queries may not result in direct clicks, the framework uses a contrastive learning approach. It strives to enhance the query-item relationship modeling by incorporating additional data through a diffusion model. This model is trained to generate probabilities of item selections based on search queries, effectively augmenting the positive item set for less frequently clicked query cases and addressing the issue of data sparsity.
3. Module Integration and Training: The overall model is trained by integrating losses from the primary CTR prediction task, next-item prediction, and the contrastive learning module. This multi-loss strategy ensures comprehensive learning from the diverse facets of user interaction data, thereby enhancing the model's ability to accurately anticipate user behaviors.

Experimental Results

The experimental analysis reveals that the proposed QueryRec framework significantly outperforms existing state-of-the-art models. By focusing on query representation learning, the model effectively transfers essential user preference information from the search domain to enhance recommendations. The empirical evaluation on datasets such as KuaiSAR and an industrial dataset demonstrate improvements in AUC scores over competing methods like SESRec and IV4Rec+.

Implications and Future Directions

The implications of this paper are multifaceted. Practically, the work offers a robust mechanism for platforms hosting both search and recommendation domains to enhance their recommender systems by deeply integrating search-derived insights. Theoretically, the paper provides a compelling case for utilizing sophisticated representation learning techniques combined with generative models to manage domain-specific challenges like data sparsity and preference shifts.

Looking forward, this research can pave the way for exploring the integration of additional contextual data and real-time adaptation strategies to further personalize recommendations. Additionally, the intersection of causal inference and generative modeling within this framework could be an exciting area to explore future enhancements.

By expertly leveraging search query data, this paper not only addresses a crucial shortcoming in contemporary recommendation technologies but also offers a scalable solution with demonstrable efficacy. As a result, it stands as a valuable contribution to the ongoing evolution of intelligent recommendation systems.