Short videos have gained immense popularity, necessitating effective recommender systems that cater to individual preferences. The platforms use advanced algorithms to analyze user engagement and provide videos that satisfy users. A critical factor in user satisfaction is immersion , defined as the feeling of being deeply engaged in an activity. However, existing recommendation algorithms in the short video scenario have largely disregarded user immersion. Our study integrates user immersion into recommendation systems, aiming to predict immersion from user interactions and recommend items to enhance the overall viewing experience. Based on the user study of collecting and analyzing user immersion, we integrate immersion into the recommendations for both lab and large-scale scenarios. We adapt user-annotated immersion to large-scale real-world datasets without immersion labels. Specifically, we propose ImmersRec , an immersion-aware recommendation framework with immersion prediction fine-tuning, immersion knowledge alignment, and immersion-enhanced recommendation. Extensive experiments on two short video platforms indicate that our approach achieves significant enhancements among various context-aware recommender backbones. We investigate the predicted immersion and find it impacts not only short-term utility but also long-term user engagement. This research pioneers the incorporation of user immersion in short video recommendation algorithms, emphasizing its potential for improving recommendations with minimal data. The code can be available at https://github.com/hezy18/ImmersRec .

The rapid growth of short videos has necessitated effective recommender systems to match users with content tailored to their evolving preferences. Current video recommendation models primarily treat each video as a whole, overlooking the dynamic nature of user preferences with specific video segments. In contrast, our research focuses on segment-level user interest modeling, which is crucial for understanding how users' preferences evolve during video browsing. To capture users' dynamic segment interests, we propose an innovative model that integrates a hybrid representation module, a multi-modal user-video encoder, and a segment interest decoder. Our model addresses the challenges of capturing dynamic interest patterns, missing segment-level labels, and fusing different modalities, achieving precise segment-level interest prediction.

Cross-domain recommendation (CDR) has emerged as a promising direction for expanding the applicability of recommendation systems. Recent advances in CDR have demonstrated the effectiveness of the unified distribution paradigm, which leverages shared distributions to transfer knowledge across domains and employs domain-specific adapters for targeted recommendations. While this well-designed paradigm shows promising performance, existing methods require extra supervision signals (e.g. contrastive learning on domain-masked embeddings) to maintain unified distributions across domains, leading to an inherent trade-off between unified objectives and domain-specific preference modeling. To address these limitations, we propose CD-CDR (Conditional Diffusion-CDR), a novel approach that leverages a shared conditional diffusion model to learn unified item distributions and facilitate knowledge transfer across domains. The key insight is to utilize the powerful generative capabilities of diffusion models to learn a shared distribution while naturally incorporating domain-specific characteristics through conditional generation. This design enables CD-CDR to replace traditional adapters with generation conditions as an integral part of the distribution model, thereby eliminating extra supervision signals and fundamentally resolving the trade-off between unified and domain-specific objectives. Extensive experiments on six domain pairs from two real-world datasets demonstrate that CD-CDR significantly outperforms existing methods for both normal and cold-start settings. To the best of our knowledge, this is the first work to explore the unified distribution paradigm in CDR using conditional diffusion models.

With an increasing number of music tracks available online, music recommender systems have become popular and ubiquitous. Previous research indicates that people’s preferences, especially in music, dynamically change with various factors, such as surrounding situations and emotional status. However, few existing public recommendation datasets contain such situation or emotion information. Therefore, we constructed SiTunes, a situational music recommendation dataset with rich physiological and psychological signals. We collected the data through a three-stage user study, including: (1) recorded users’ inherent music preference in a lab setting (Stage 1), (2) recorded physiological and environmental situations by smart wristband devices in users’ daily life, and provided psychological and rating feedback for music recommended by traditional recommenders (Stage 2) and (3) by situation-aware recommenders (Stage 3). The experiments were conducted with strict privacy concerns and ethical approval. The dataset contains over 2000 listening logs from 30 users on over 300 music tracks. SiTunes serves as a valuable resource for future studies on situational recommenders and user understanding in recommendation. The dataset is available at https://github.com/JiayuLi-997/SiTunes_dataset/.

With the applications of recommendation systems rapidly expanding, an increasing number of studies have focused on every aspect of recommender systems with different data inputs, models, and task settings. Therefore, a flexible library is needed to help researchers implement the experimental strategies they require. Existing open libraries for recommendation scenarios have enabled reproducing various recommendation methods and provided standard implementations. However, these libraries often impose certain restrictions on data and seldom support the same model to perform different tasks and input formats, limiting users from customized explorations. To fill the gap, we propose ReChorus2.0, a modular and task-flexible library for recommendation researchers. Based on ReChorus, we upgrade the supported input formats, models, and training&evaluation strategies to help realize more recommendation tasks with more data types. The main contributions of ReChorus2.0 include: (1) Realization of complex and practical tasks, including re-ranking and CTR prediction tasks; (2) Inclusion of various context-aware and re-ranking recommenders; (3) Extension of existing and new models to support different tasks with the same models; (4) Support of highly-customized input with impression logs, negative items, or click labels, as well as user, item, and situation contexts. To summarize, ReChorus2.0 serves as a comprehensive and flexible library that better addresses the practical problems in the recommendation scenario and caters to more diverse research needs. The implementation and detailed tutorials of ReChorus2.0 can be found at https://github.com/THUwangcy/ReChorus.

From e-commerce to music and news, recommender systems are tailored to specific scenarios. While researching generic models applicable to various scenarios is crucial, studying recommendations based on the unique characteristics of a specific and vital scenario holds both research and, more importantly, practical value.

As recommender systems become pervasive in various scenarios, cross-domain recommenders (CDR) are proposed to enhance the performance of one target domain with data from other related source domains. However, irrelevant information from the source domain may instead degrade target domain performance, which is known as the negative transfer problem. Most existing efforts to tackle this issue primarily focus on designing adaptive representations for overlapped users. Whereas, these methods rely on the learned representations of the model, lacking explicit constraints to filter irrelevant source-domain collaborative information for the target domain, which limits their cross-domain transfer capability.

Short video~(SV) online streaming has been one of the most popular Internet applications in recent years. When browsing SVs, users gradually immerse themselves and derive relaxation or knowledge. Whereas prolonged browsing will lead to a decline in positive feelings, users continue due to inertia, resulting in decreased satisfaction. Immersion is shown to be an essential factor for users' positive experience and highly related to users' interactions in film, games, and virtual reality. However, immersion in SV interaction is still unexplored, which differs from the previously studied scenarios essentially because SV delivery is fragmented, discrete, and with limited time for each video.

Ranking ensemble is a critical component in real recommender systems. When a user visits a platform, the system will prepare several item lists, each of which is generally from a single behavior objective recommendation model. As multiple behavior intents, e.g., both clicking and buying some specific item category, are commonly concurrent in a user visit, it is necessary to integrate multiple single-objective ranking lists into one. However, previous work on rank aggregation mainly focused on fusing homogeneous item lists with the same objective while ignoring ensemble of heterogeneous lists ranked with different objectives with various user intents.

In recent years, context information has played an increasingly significant role in recommendation systems. With the rapid growth of portable sensor devices, lifelog data, such as mood, location, and daily activity, has been recorded and used for ubiquitous recommendation tasks. However, since the multi-modal lifelog data contains objective context information and subjective user labeling, it is challenging to record the lifelog thoroughly and perform personalized recommendations in real-time. In this work, we design a mobile application (App), LifeRec, to record multi-modal lifelog data and perform personalized recommendations by communicating with the remote server. The App helps users collect various lifelog information (e.g., location, diet, activity, and mood) and receive real-time recommendation with privacy protection and little effort. It is useful for lifelog data collection, user status monitoring, and various ubiquitous recommendation tasks. We examine LifeRec in a one-week field study with seven subjects. The users’ experience feedback and recording results show great usability and task completeness with our App.

The item fairness issue has become one of the significant concerns with the development of recommender systems in recent years, focusing on whether items' exposures are consistent with their utilities. So the measurement of item unfairness depends on the modeling of item utility, and most previous approaches estimated item utility simply based on user-item interaction logs in recommender systems. The Click-through rate (CTR) is the most popular one. However, we argue that these types of item utilities (named observed utility here) measurements may result in unfair exposures of items. The number of exposure for each item is uneven, and recommendation methods select the exposure audiences (users).

User's leaving from the system without further return, called user churn, is a severe negative signal in online games. Therefore, churn prediction and intervention are of great value for improving players' experiences and system performance. However, the problem has not been well-studied in the game scenario. Especially, some crucial factors, such as game difficulty, have not been considered for large-scale churn analysis. In this paper, a novel Difficulty-Aware Framework (DAF) for churn prediction and intervention is proposed. Firstly, a Difficulty Flow for each user is proposed, which is utilized to derive users' Personalized Perceived Difficulty during the game process. Then, a survival analysis modelD-Cox-Time is designed to model the Dynamic Influence of Perceived Difficulty on player churn intention. Finally, thePersonalized Perceived Difficulty ~(PPD) andDynamic Difficulty Influence ~(DDI) are incorporated to churn prediction and intervention. The proposed DAF framework has been specified in a real-world puzzle game as an example for churn prediction and intervention. Extensive offline experiments show significant improvements in churn prediction by introducing difficulty-related features. Besides, we conduct an online intervention system to adjust difficulty dynamically in the online game. A/B test results verify that the proposed intervention system enhances user retention and engagement significantly. To the best of our knowledge, it is the first framework in games that illustrates an in-depth understanding and leveraging dynamic and personalized perceived difficulty during game playing, which is easy to be integrated with various churn prediction and intervention models.