Streaming media has become a popular medium for consumers of all ages, with people spending several hours a day streaming videos, games, music, audiobooks or podcasts across devices. Most global streaming services have introduced Generative Artificial Intelligence (GenAI) into their operations to personalize consumer experience, improve content, and further enhance the value proposition of streaming services. Despite the rapid growth, there is a need to bridge the gap between academic research and industry requirements and build connections between researchers and practitioners in the field. This workshop aims to provide a unique forum for practitioners and researchers interested in GenAI to get together, exchange ideas and get a pulse for the state of the art in research and burning issues in the industry.

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 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 systems (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 (multi-modal) LLMs in RecSys, in this new edition, we decide to formally add the new topic of (multi-modal) LLM models in OARS. We will invite experts and papers in the field to disseminate new knowledge and foster further advancements.

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.

Music recommendation has been a prominent use case in the Rec-Sys community since the early days [4,14].With the growth of music streaming platforms in the last twenty years, algorithmic recommendation became critically important for the music industry.For consumers, when tenth of millions of music items are readily available, recommender systems are absolutely essential in helping to reduce the choice overload.Further, beyond assisting the listener in their music discovery, recommender systems have expanded to many aspects of the musical experience.A virtuous influential circle between the music industry and technological research drove improvements both in music listening experiences and in general scientific knowledge in recommender systems.Many fundamental topics in RecSys have matured together with their applications in music streaming (e.g.collaborative filtering, user modeling, etc.), while some distinctive aspects of the music medium (i.e.often consumed in sequence, passively, re-recommendation possible, etc. [13]) drove their own specific topics, such as playlist generation [5] or next-song recommendation [15].Today, music recommendation is a vibrant research area, prolific with respect to new topics [13] that led to novel contributions to the RecSys community.For example, in 2022 one of the best paper awards focused on understanding ways that recommendation

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.

Voice-based conversational recommenders offer a natural way to improve recommendation quality by asking the user for missing information. This talk details how we use offline policy optimization to learn a dialog manager that determines what items to present and what clarifying questions to ask, in order to maximize the success of the conversation. Counter-factual learning allows us to compare various modeling techniques using only logged conversational data. Our approach is applied to Amazon Music’s first voice browsing experience (Alexa, help me find music), which interleaves disambiguation questions and music sample suggestions. Offline policy evaluation results show that an XGBoost reward regressor outperforms linear and neural policies on held out data. A first user-facing A/B test confirms our offline results, by increasing our task completion rate by 8% relative compared to our production rule-based conversational recommender, while reducing the number of turns to complete the task by 20%. A second A/B test shows that extending the set of candidate items to present and adding an embedding-based user-item affinity action feature improves task success rate further by 4% relative, while reducing the number of turns further by 13%. These results suggest that offline policy optimization from conversation logs is a viable way to foster conversational recommender research, while minimizing the number of user-facing experiments needed to determine the optimal dialog policy.

With the increase of data collected and computation power available, modern recommender systems are ever facing new challenges. While complex models are developed in academia, industry practice seems to focus on relatively simple techniques that can deal with the magnitude of data and the need to distribute the computation. The workshop on large-scale recommender systems (LSRS) is a meeting place for industry and academia to discuss the current and future challenges of applied large-scale recommender systems.

With the increase of data collected and computation power available, modern recommender systems are ever facing new challenges. While complex models are developed in academia, industry practice seems to focus on relatively simple techniques that can deal with the magnitude of data and the need to distribute the computation. The workshop on large-scale recommender systems (LSRS) is a meeting place for industry and academia to discuss the current and future challenges of applied large-scale recommender systems.

With the increase of data collected and computation power available, modern recommender systems are ever facing new challenges. While complex models are developed in academia, industry practice seems to focus on relatively simple techniques that can deal with the magnitude of data and the need to distribute the computation. The workshop on large-scale recommender systems (LSRS) is a meeting place for industry and academia to discuss the current and future challenges of applied large-scale recommender systems.

With the increase of data collected and computation power available, modern recommender systems are ever facing new challenges. While complex models are developed in academia, industry practice seems to focus on relatively simple techniques that can deal with the magnitude of data and the need to distribute the computation. The first workshop on large-scale recommender systems (LSRS) is a meeting place for industry and academia to discuss the current and future challenges of applied large-scale recommender systems.