Dingxian Wang

E-commerce content generation necessitates creating engaging and customer-centric material to endorse products and enhance user satisfaction. Existing methods depend on task-specific feature design, which requires a fine-tailored model for each task with complex data collection and pre-processing, and their generation capabilities are limited. Meanwhile, large language models have demonstrated their capabilities in diverse natural language processing tasks, solving multiple tasks in a unified process. To address the concerns in e-commerce content generation, we leverage the impressive generation performance of large language models and propose a framework to educate them as proficient promoters in various e-commerce-related tasks. Our framework involves two modules: self-educating proliferates task instructions and data by instructing the unaligned model, and multi-aspect instruction alignment educates the language model by embedding all e-commerce tasks in a unified framework. The proposed model, Promoter, can perform a batch of prediction and generation tasks, working as a smart and creative promoter that only requires a quick view of the customer profile. Extensive experiments from automatic and human perspectives indicate that Promoter achieves state-of-the-art performances in various generation tasks, bringing the productivity of large language models to e-commerce in an integrated pipeline.

The "Gen AI for E-commerce" workshop explores the role of Generative Artificial Intelligence in transforming e-commerce through enhanced user experience and operational efficiency. E-commerce companies grapple with multiple challenges such as lack of quality content for products, subpar user experience, sparse datasets etc. Gen AI offers significant potential to address these complexities. Yet, deploying these technologies at scale presents challenges such as hallucination in data, excessive costs, increased latency response, and limited generalization in sparse data environments. This workshop will bring together experts from academia and industry to discuss these challenges and opportunities, aiming to showcase case studies, breakthroughs, and insights into practical implementations of Gen AI in e-commerce.

Product titles on e-commerce marketplaces often suffer from verbosity and inaccuracy, hindering effective communication of essential product details to customers. Refining titles to be more concise and informative is crucial for better user experience and product promotion. Recent solutions to product title refinement follow the standard text extractive and generative methods. Some also leverage multimodal information, e.g. using product images to supplement original titles with visual knowledge. However, these generative methods often produce additional terms not endorsed by sellers. Thus, it remains challenging to incorporate visual information missing from original titles into refined titles without excessively introducing novel terms. Additionally, most existing methods require human-labeled datasets, which are laborious to construct. In response to the two challenges, we present a self-supervised multimodal framework (HLATR) for title refinement that comprises two key modules: (1) a perturbated sample generator that constructs training data by systematically mining homogeneous listing information and (2) a title refinement network that effectively harnesses visual information to refine the original titles. To explicitly balance the extraction from original titles and the generation of supplementary novel terms, we adapt the copy mechanism that is guided by a focused refinement loss. Extensive experiments demonstrate that our proposed framework consistently outperforms others in generating refined titles that contain essential multimodal semantics with minimal deviation from the original ones.

Conventional recommender systems (RSs) face challenges in precisely capturing users' fine-grained preferences. Large language models (LLMs) have shown capabilities in commonsense reasoning and leveraging external tools that may help address these challenges. However, existing LLM-based RSs suffer from hallucinations, misalignment between the semantic space of items and the behavior space of users, or overly simplistic control strategies (e.g., whether to rank or directly present existing results). To bridge these gap, we introduce ToolRec, a framework for LLM-empowered recommendations via tool learning that uses LLMs as surrogate users, thereby guiding the recommendation process and invoking external tools to generate a recommendation list that aligns closely with users' nuanced preferences. We formulate the recommendation process as a process aimed at exploring user interests in attribute granularity. The process factors in the nuances of the context and user preferences. The LLM then invokes external tools based on a user's attribute instructions and probes different segments of the item pool. We consider two types of attribute-oriented tools: rank tools and retrieval tools. Through the integration of LLMs, ToolRec enables conventional recommender systems to become external tools with a natural language interface. Extensive experiments verify the effectiveness of ToolRec, particularly in scenarios that are rich in semantic content.

Recommender systems suffer from confounding biases when there exist confounders affecting both item features and user feedback (e.g., like or not). Existing causal recommendation methods typically assume confounders are fully observed and measured, forgoing the possible existence of hidden confounders in real applications. For instance, product quality is a confounder since it affects both item prices and user ratings, but is hidden for the third-party e-commerce platform due to the difficulty of large-scale quality inspection; ignoring it could result in the bias effect of over-recommending high-price items. This work analyzes and addresses the problem from a causal perspective. The key lies in modeling the causal effect of item features on a user's feedback. To mitigate hidden confounding effects, it is compulsory but challenging to estimate the causal effect without measuring the confounder. Towards this goal, we propose a Hidden Confounder Removal (HCR) framework that leverages front-door adjustment to decompose the causal effect into two partial effects, according to the mediators between item features and user feedback. The partial effects are independent from the hidden confounder and identifiable. During training, HCR performs multi-task learning to infer the partial effects from historical interactions. We instantiate HCR for two scenarios and conduct experiments on three real-world datasets. Empirical results show that the HCR framework provides more accurate recommendations, especially for less-active users. We will release the code once accepted.

Click-Through Rate (CTR) prediction plays a core role in recommender systems, serving as the final-stage filter to rank items for a user. The key to addressing the CTR task is learning feature interactions that are useful for prediction, which is typically achieved by fitting historical click data with the Empirical Risk Minimization (ERM) paradigm. Representative methods include Factorization Machines and Deep Interest Network, which have achieved wide success in industrial applications. However, such a manner inevitably learns unstable feature interactions, i.e., the ones that exhibit strong correlations in historical data but generalize poorly for future serving. In this work, we reformulate the CTR task --- instead of pursuing ERM on historical data, we split the historical data chronologically into several periods (a.k.a, environments), aiming to learn feature interactions that are stable across periods. Such feature interactions are supposed to generalize better to predict future behavior data. Nevertheless, a technical challenge is that existing invariant learning solutions like Invariant Risk Minimization are not applicable, since the click data entangles both environment-invariant and environment-specific correlations. To address this dilemma, we propose Disentangled Invariant Learning (DIL) which disentangles feature embeddings to capture the two types of correlations separately. To improve the modeling efficiency, we further design LightDIL which performs the disentanglement at the higher level of the feature field. Extensive experiments demonstrate the effectiveness of DIL in learning stable feature interactions for CTR.