E-commerce live streaming as an increasingly popular sales model has generated a significant amount of gross merchandise value (GMV) for e-commerce platforms. Live streaming recommendation systems (LSRS) of e-commerce aim to recommend the most appropriate live channels for users to motivate them to buy products. Existing LSRS methods focus only on the user’s interaction behaviors on the live channel (live domain) while ignoring the user’s behaviors and intentions on the e-commerce product (product domain). As a result, the user’s consistent purchase intentions in the cross-domain are not being fully captured, especially when user present differentiated purchase intentions in the cross-domain. How to disentangle user’s consistent intentions and domain-specific intentions in the cross-domain poses a challenge to the LSRS of e-commerce platforms. In this paper, we present a live channel recommendation method, named eLiveRec, developed for Taobao, one of the largest e-commerce platform in the world. Specifically, eLiveRec employs the disentangled encoder module to learn user’s cross-domain consistent intentions and domain-specific intentions. Then, an adaptive multi-task learning framework is developed to jointly optimize the multiple objectives (e.g., stay time, click goods bag, and click products after entering channel) related to live streaming recommendation. In this way, the performance of live streaming recommendation can be further improved and con-form to standard industry RS paradigms. Extensive experiments are conducted on a large-scale industry dataset collected from Taobao Live platform have been performed. Both online and offline experimental results indicate that eLiveRec consistently outperforms existing state-of-the-art baseline methods.

With the accumulation of check-in data from location-based services, next Point-of-Interest (POI) recommendations are gaining increasing attention. It is well known that the spatio-temporal contextual information of user check-in behavior plays a crucial role in handling vital and inherent challenges in next POI recommendation, including capture of user dynamic preferences and the sparsity problem of check-in data. However, many studies either ignore or simply stack the context features with the embedding of POIs while relying only on POI recommendation loss to optimize the entire model, therefore failing to take full advantage of the potential information in contexts. Additionally, users’ interests are usually unstable and evolve over time, and accordingly recent studies have proposed various approaches to predict users’ next POIs by incorporating contextual information and modeling both their long- and short-term preferences, respectively. Yet many studies overemphasize the final POI recommendation performance, and the association between POI sequences and contextual information is not well embodied in data representations. In this article, we focus on the preceding problems and propose a unified attention framework for next POI recommendation by modeling users’ Long- and Short-term Preferences via Self-supervised Learning (LSPSL). Specifically, based on the self-attention network and two self-supervised optimization objectives, LSPSL first deeply exploits the intrinsic correlations between POI sequences and contextual information through pre-training, which strengthens data representations. Then, supported by pre-trained contextualized embeddings, LSPSL models and fuses users’ complex long- and short-term preferences in a unified way. Extensive experiments on real-world datasets demonstrate the superiority of our model compared with other state-of-the-art approaches.

Accurate user interest modeling is important for news recommendation. Most existing methods for news recommendation rely on implicit feedbacks like click for inferring user interests and model training. However, click behaviors usually contain heavy noise, and cannot help infer complicated user interest such as dislike. Besides, the feed recommendation models trained solely on click behaviors cannot optimize other objectives such as user engagement. In this paper, we present a news feed recommendation method that can exploit various kinds of user feedbacks to enhance both user interest modeling and model training. We propose a unified user modeling framework to incorporate various explicit and implicit user feedbacks to infer both positive and negative user interests. In addition, we propose a strong-to-weak attention network that uses the representations of stronger feedbacks to distill positive and negative user interests from implicit weak feedbacks for accurate user interest modeling. Besides, we propose a multi-feedback model training framework to learn an engagement-aware feed recommendation model. Extensive experiments on a real-world dataset show that our approach can effectively improve the model performance in terms of both news clicks and user engagement.

Abstract Nowadays recording and sharing personal lives using mobile devices on the Internet is becoming increasingly popular, and successive POI recommendation is gaining growing attention from academia and industry. In mobile scenarios, multiple influencing factors including the diversity of user preferences, the changeability of user behavior and the dynamic of spatiotemporal context bring great challenges to the POI recommender system. In order to accurately capture both the stable and the contextual preferences of mobile users in dynamic contexts, we propose a fusion framework JANICP (Joint Attention Networks with Inherent and Contextual Preferences) for successive POI recommendation by jointly training an offline/nearline user inherent interest perception model and an online user contextual interest prediction model. The offline model is trained based on the global historical behavior data to achieve stable interest representation, while the online model is trained based on the instantly selected context-sensitive data to achieve dynamic interest perception. An attention aggregation and matching module is used to fully connect the two kinds of preference representations and generate the final POI recommendation. Extensive experiments were conducted on three real datasets and experimental results show that the proposed JANICP outperforms existing state-of-the-art methods.

Abstract Microtask crowdsourcing is a form of crowdsourcing in which work is decomposed into a set of small, self-contained tasks, which each can typically be completed in a matter of minutes. Due to the various capabilities and knowledge background of the voluntary participants on the Internet, the answers collected from the crowd are ambiguous and the final answer aggregation is challenging. In this process, the choice of quality control strategies is important for ensuring the quality of the crowdsourcing results. Previous work on answer estimation mainly used expectation–maximization (EM) approach. Unfortunately, EM provides local optimal solutions and the estimated results will be affected by the initial value. In this paper, we extend the local optimal result of EM and propose an approximate global optimal algorithm for answer aggregation of crowdsourcing microtasks with binary answers. Our algorithm is expected to improve the accuracy of real answer estimation through further likelihood maximization. First, three worker quality evaluation models are presented based on static and dynamic methods, respectively, and the local optimal results are obtained based on the maximum likelihood estimation method. Then, a dominance ordering model (DOM) is proposed according to the known worker responses and worker categories for the specified crowdsourcing task to reduce the space of potential task-response sequence while retaining the dominant sequence. Subsequently, a Cut-point neighbor detection algorithm is designed to iteratively search for the approximate global optimal estimation in a reduced space, which works on the proposed dominance ordering model (DOM). We conduct extensive experiments on both simulated and real-world datasets, and the experimental results illustrate that the proposed approach can obtain better estimation results and has higher performance than regular EM-based algorithms.