Recommender system (RecSys) plays important roles in helping users navigate, discover, and consume massive and highly-dynamic information. Today, many RecSys solutions deployed in the real world rely on categorical user-profiles and/or pre-calculated recommendation actions that stay static during a user session. However, recent trends suggest that RecSys need to model user intent in real time and constantly adapt to meet user needs at the moment or change user behavior in situ. There are three primary drivers for this emerging need of online adaptation. First, in order to meet the increasing demand for a better personalized experience, the personalization dimensions and space will grow larger and larger. It would not be feasible to pre-compute recommended actions for all personalization scenarios beyond a certain scale. Second, in many settings the system does not have user prior history to leverage. Estimating user intent in real time is the only feasible way to personalize. As various consumer privacy laws tighten, it is foreseeable that many businesses will reduce their reliance on static user profiles. Therefore, it makes the modeling of user intent in real time an important research topic. Third, a user's intent often changes within a session and between sessions, and user behavior could shift significantly during dramatic events. Therefore, it is important to investigate more on online and adaptive recommender system (OARS) that can adapt in real time to meet user needs and be robust against distribution shifts. Every year, the organizers survey the most important topics for OARS and propose a new workshop program. In light of the recent advancement of LLMs and foundation models in RecSys, in this new edition, we decide to formally add the new topic of foundation and LLM models in OARS. We will invite experts and papers in the field to facilitate its further advancement. Our workshop offers a focused discussion of the new study and application of OARS, and will bring together an interdisciplinary community of researchers and practitioners from both industry and academia to discuss on new topics in the area, grow a community, and push the direction forward.

Recommender systems (RecSys) play important roles in helping users navigate, discover, and consume large and highly dynamic information. Today, many RecSys solutions deployed in the real world rely on categorical user-profiles and/or pre-calculated recommendation actions that stay static during a user session. However, recent trends suggest that RecSys need to model user intent in real time and constantly adapt to meet user needs at the moment or change user behavior in situ. There are three primary drivers for this emerging need of online adaptation. First, in order to meet the increasing demand for a better personalized experience, the personalization dimensions and space will grow larger and larger. It would not be feasible to pre-compute recommended actions for all personalization scenarios beyond a certain scale. Second, in many settings the system does not have user prior history to leverage. Estimating user intent in real time is the only feasible way to personalize. As various consumer privacy laws tighten, it is foreseeable that many businesses will reduce their reliance on static user profiles. Therefore, it makes the modeling of user intent in real time an important research topic. Third, a user's intent often changes within a session and between sessions, and user behavior could shift significantly during dramatic events. A RecSys should adapt in real time to meet user needs and be robust against distribution shifts. The online and adaptive recommender systems (OARS) workshop offers a focused discussion of the study and application of OARS, and will bring together an interdisciplinary community of researchers and practitioners from both industry and academia. KDD, as the premier data science conference, is an ideal venue to gather leaders in the field to further research into OARS and promote its adoption. This workshop is complementary to several sessions of the main conference (e.g., recommendation, reinforcement learning, etc.) and brings them together using a practical and focused application.

Recommender systems (RecSys) play important roles in helping users navigate, discover, and consume large and highly dynamic information. Today, many RecSys solutions deployed in the real world rely on categorical user-profiles and/or pre-calculated recommendation actions that stay static during a user session. However, recent trends suggest that RecSys need to model user intent in real time and constantly adapt to meet user needs at the moment or change user behavior in situ. There are three primary drivers for this emerging need of online adaptation. First, in order to meet the increasing demand for a better personalized experience, the personalization dimensions and space will grow larger and larger. It would not be feasible to pre-compute recommended actions for all personalization scenarios beyond a certain scale. Second, in many settings the system does not have user prior history to leverage. Estimating user intent in real time is the only feasible way to personalize. As various consumer privacy laws tighten, it is foreseeable that many businesses will reduce their reliance on static user profiles. Therefore, it makes the modeling of user intent in real time an important research topic. Third, a user's intent often changes within a session and between sessions, and user behavior could shift significantly during dramatic events. A RecSys should adapt in real time to meet user needs and be robust against distribution shifts. The online and adaptive recommender systems (OARS) workshop offers a focused discussion of the study and application of OARS, and will bring together an interdisciplinary community of researchers and practitioners from both industry and academia. KDD, as the premier data science conference, is an ideal venue to gather leaders in the field to further research into OARS and promote its adoption. This workshop is complementary to several sessions of the main conference (e.g., recommendation, reinforcement learning, etc.) and brings them together using a practical and focused application.

Many recommender systems deployed in the real world rely on categorical user-profiles and/or pre-calculated recommendation actions that stay static during a user session. Recent trends suggest that recommender systems should model user intent in real time and constantly adapt to meet user needs at the moment or change user behavior in situ. In addition, there have been many advances that make online and adaptive recommender systems (OARS) feasible, scalable, and more sophisticated. This workshop aims to bring together practitioners and researchers from academia and industry to discuss the challenges and approaches to implement OARS algorithms and systems and improve user experiences by better modeling and responding to user intent.

Query categorization is an essential part of query intent understanding in e-commerce search. A common query categorization task is to select the relevant fine-grained product categories in a product taxonomy. For frequent queries, rich customer behavior (e.g., click-through data) can be used to infer the relevant product categories. However, for more rare queries, which cover a large volume of search traffic, relying solely on customer behavior may not suffice due to the lack of this signal. To improve categorization of rare queries, we adapt the Pseudo-Relevance Feedback (PRF) approach to utilize the latent knowledge embedded in semantically or lexically similar product documents to enrich the representation of the more rare queries. To this end, we propose a novel deep neural model named Attentive Pseudo Relevance Feedback Network (APRF-Net) to enhance the representation of rare queries for query categorization. To demonstrate the effectiveness of our approach, we collect search queries from a large commercial search engine, and compare APRF-Net to state-of-the-art deep learning models for text classification. Our results show that the APRF-Net significantly improves query categorization by 5.9% on [email protected] score over the baselines, which increases to 8.2% improvement for the rare (tail) queries. The findings of this paper can be leveraged for further improvements in search query representation and understanding.

Users' clicks on Web search results are one of the key signals for evaluating and improving web search quality and have been widely used as part of current state-of-the-art Learning-To-Rank(LTR) models. With a large volume of search logs available for major search engines, effective models of searcher click behavior have emerged to evaluate and train LTR models. However, when modeling the users' click behavior, considering the bias of the behavior is imperative. In particular, when a search result is not clicked, it is not necessarily chosen as not relevant by the user, but instead could have been simply missed, especially for lower-ranked results. These kinds of biases in the click log data can be incorporated into the click models, propagating the errors to the resulting LTR ranking models or evaluation metrics. In this paper, we propose the De-biased Reinforcement Learning Click model (DRLC). The DRLC model relaxes previously made assumptions about the users' examination behavior and resulting latent states. To implement the DRLC model, convolutional neural networks are used as the value networks for reinforcement learning, trained to learn a policy to reduce bias in the click logs. To demonstrate the effectiveness of the DRLC model, we first compare performance with the previous state-of-art approaches using established click prediction metrics, including log-likelihood and perplexity. We further show that DRLC also leads to improvements in ranking performance. Our experiments demonstrate the effectiveness of the DRLC model in learning to reduce bias in click logs, leading to improved modeling performance and showing the potential for using DRLC for improving Web search quality.

Recommendation systems have been crucial in driving revenue in e-commerce especially in online retail. Complementary item recommendation is a challenging problem within this field due to the inherent difficulty in defining how products relate to each other. In this paper we design and propose a complementary item recommendation system that uses multi-modal embeddings (both text-based and image-based). Our system is carefully designed and validated by domain experts. We provide extensive insights into our design choices and what does not work in terms of model choice and features used. We successfully demonstrate our system on two challenging datasets from a home improvement retailer: one involving outdoor furniture (patio) and the second involving bathroom products. When deployed live on the website, our model resulted in +170% increase in engaged visits to the recommendation container.

We address the problem of learning how compatible two products are. Assessing compatibility is challenging because the meaning of compatibility changes depending on product categories. In this study, we leverage domain experts' knowledge to generate labels and datasets. Next, we engineer 58 different features from product titles and product descriptions. We experiment with both tree-based and deep learning classifiers using different sets of features to capture compatibility patterns across four product categories. Although the performance does not show consistent pattern across all categories, the precision and recall of the best algorithm from most categories are above 90%. In addition, we find that the performance of classifiers are in general satisfactory. Based on human validation, few best-performing classifiers demonstrate better performance than labels generated from domain experts.

E-commerce applications rely heavily on session-based recommendation algorithms to improve the shopping experience of their customers. Recent progress in session-based recommendation algorithms shows great promise. However, translating that promise to real-world outcomes is a challenging task for several reasons, but mostly due to the large number and varying characteristics of the available models. In this paper, we discuss the approach and lessons learned from the process of identifying and deploying a successful session-based recommendation algorithm for a leading e-commerce application in the home-improvement domain. To this end, we initially evaluate fourteen session-based recommendation algorithms in an offline setting using eight different popular evaluation metrics on three datasets. The results indicate that offline evaluation does not provide enough insight to make an informed decision since there is no clear winning method on all metrics. Additionally, we observe that standard offline evaluation metrics fall short for this application. Specifically, they reward an algorithm only when it predicts the exact same item that the user clicked next or eventually purchased. In a practical scenario, however, there are near-identical products which, although they are assigned different identifiers, they should be considered as equally-good recommendations. To overcome these limitations, we perform an additional round of evaluation, where human experts provide both objective and subjective feedback for the recommendations of five algorithms that performed the best in the offline evaluation. We find that the experts’ opinion is oftentimes different from the offline evaluation results. Analysis of the feedback confirms that the performance of all models is significantly higher when we evaluate near-identical product recommendations as relevant. Finally, we run an A/B test with one of the models that performed the best in the human evaluation phase. The treatment model increased conversion rate by 15.6% and revenue per visit by 18.5% when compared with a leading third-party solution.

Retail industry has been disrupted by the e-commerce revolution more than any other industry. Some giant retailers went out of business or filed for bankruptcy as a result of that like Sears and Toys R Us. However, some verticals in the retail industry are still robust and not been disrupted due to the lack of e-commerce solutions that convinced customers to turn their back to the existing physical stores in favor of the online experience. Home improvement is the best example of such vertical where e-commerce has not "yet" disrupted the domain and caused problems to the leading companies which still rely heavily on physical stores.

Recommender systems are an integral part of eCommerce services, helping to optimize revenue and user satisfaction. Bundle recommendation has recently gained attention by the research community since behavioral data supports that users often buy more than one product in a single transaction. In most cases, bundle recommendations are of the form "users who bought product A also bought products B, C, and D". Although such recommendations can be useful, there is no guarantee that products A, B, C, and D may actually be related to each other. In this paper, we address the problem of collection recommendation, i.e., recommending a collection of products that share a common theme and can potentially be purchased together in a single transaction. We extend on traditional approaches that use mostly transactional data by incorporating both domain knowledge from product suppliers in the form of hierarchies, as well as textual attributes from the products. Our approach starts by combining product hierarchies together with transactional data or domain knowledge to identify candidate sets of product collections. Then, it generates the product collection recommendations from these candidate sets by learning a deep similarity model that leverages textual attributes. Experimental evaluation on real data from the Home Depot online retailer shows that the proposed solution can recommend collections of products with increased accuracy when compared to expert-crafted collections.