In video search systems, user historical behaviors provide rich context for identifying search intent and resolving ambiguity. However, traditional methods utilizing implicit history features often suffer from signal dilution and delayed feedback. To address these challenges, we propose WeWrite, a novel Personalized Demand-aware Query Rewriting framework. Specifically, WeWrite tackles three key challenges: (1) When to Write: An automated posterior-based mining strategy extracts high-quality samples from user logs, identifying scenarios where personalization is strictly necessary; (2) How to Write: A hybrid training paradigm combines Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO) to align the LLM's output style with the retrieval system; (3) Deployment: A parallel "Fake Recall" architecture ensures low latency. Online A/B testing on a large-scale video platform demonstrates that WeWrite improves the Click-Through Video Volume (VV$>$10s) by 1.07% and reduces the Query Reformulation Rate by 2.97%.

Search and Recommendation (S&R) are core information access channels on modern multi-scenario platforms. Existing joint S&R models face two critical challenges: (1) cross-scenario interest inconsistency, failing to unify explicit search intent (queries) and implicit recommendation intent (behavioral interactions) into coherent user interest representations; (2) severe S&R trade-off, where enhancing one task degrades the other due to static knowledge sharing and unbalanced feature utilization. To address these issues, we propose MinSAR, a novel framework focusing on cross-S&R user interest consistency. It integrates two key innovations: a Unified Interest Generation (UIG) module using Vector Quantized-Variational Autoencoder (VQ-VAE) to fuse long-term user preferences (via a user-specific memory network) and dynamic short-term contextual behaviors, generating compact cross-scenario latent representations that bridge explicit and implicit intents. Additionally, an Interest-Guided Attention Expert Network replaces static multi-task gating with intent-aware weight allocation. Guided by UIG's unified interest, it dynamically balances cross-S&R shared knowledge and task-specific expertise (semantic matching for search, collaborative filtering for recommendation), mitigating inter-task conflicts. Extensive experiments on two real-world datasets (KuaiSAR and Amazon Kindle Store) against 13 baselines show MinSAR outperforms state-of-the-art joint S&R models. Further analysis confirms its ability to eliminate the S&R performance trade-off.

Sentiment Quantification aims to detect the overall sentiment polarity of users from a set of reviews corresponding to a target. Existing methods equally treat and aggregate individual reviews' sentiment to judge the overall sentiment polarity. However, the confidence of each review is not equal in sentiment quantification where sentiment perturbation arising from high- and low-confidence reviews may degrade the accuracy of Sentiment Quantification. Specifically, fake reviews with deceptive sentiments are low confidence, which perturbs the overall sentiment prediction. Whereas, some reviews generated by responsible users are high confidence. They contain authoritative suggestions so they should be emphasized in Sentiment Quantification. In this paper, we design and build COSE, a confidence-aware sentiment quantification framework, which can measure the confidence of individual reviews to eliminate sentiment perturbation and facilitate sentiment quantification. We design a Review Graph that achieves review confidence modeling in an unsupervised manner and obtains review confidence representations. Moreover, we develop a dynamic fusion attention mechanism, which produces sentiment “de-perturbation” vectors to eliminate the sentiment perturbation based on the confidence representations. Extensive experiments on large-scale review datasets validate the significant superiority of COSE over the state-of-the-art.

In addition to relevance, diversity is an important yet less studied performance metric of cross-modal image retrieval systems, which is critical to user experience. Existing solutions for diversity-aware image retrieval either explicitly post-process the raw retrieval results from standard retrieval systems or try to learn multi-vector representations of images to represent their diverse semantics. However, neither of them is good enough to balance relevance and diversity. On the one hand, standard retrieval systems are usually biased to common semantics and seldom exploit diversity-aware regularization in training, which makes it difficult to promote diversity by post-processing. On the other hand, multi-vector representation methods are not guaranteed to learn robust multiple projections. As a result, irrelevant images and images of rare or unique semantics may be projected inappropriately, which degrades the relevance and diversity of the results generated by some typical algorithms like top-k. To cope with these problems, this paper presents a new method called CoLT that tries to generate much more representative and robust representations for accurately classifying images. Specifically, CoLT first extracts semantics-aware image features by enhancing the preliminary representations of an existing one-to-one cross-modal system with semantics-aware contrastive learning. Then, a transformer-based token classifier is developed to subsume all the features into their corresponding categories. Finally, a post-processing algorithm is designed to retrieve images from each category to form the final retrieval result. Extensive experiments on two real-world datasets Div400 and Div150Cred show that CoLT can effectively boost diversity, and outperforms the existing methods as a whole (with a higher F1 score).

In general, videos are powerful at recording physical patterns (e.g., spatial layout) while texts are great at describing abstract symbols (e.g., emotion). When video and text are used in multi-modal tasks, they are claimed to be complementary and their distinct information is crucial. However, when it comes to cross-modal tasks (e.g., retrieval), existing works usually use their common part in the form of common space learning while their distinct information is abandoned. In this paper, we argue that distinct information is also beneficial for cross-modal retrieval. To address this problem, we propose a divide-and-conquer learning approach, namely Complementarity-aware Space Learning (CSL), by recasting this challenge into learning of two spaces (i.e., latent and symbolic spaces) to simultaneously explore their common and distinct information by considering multi-modal complementary character. Specifically, we first propose to learn a symbolic space from video with a memory-based video encoder and a symbolic generator. In contrast, we also introduce learning a latent space from text with a text encoder and a memory-based latent feature selector. Finally, we propose a complementarity-aware loss by integrating two spaces to facilitate video-text retrieval tasks. Extensive experiments show that our approach outperforms existing state-of-the-art methods by 5.1%, 2.1% and 0.9% of R@10 for text-to-video retrieval on three benchmarks, respectively. Ablation study also verifies that the distinct information from video and text improves the retrieval performance. Trained models and source code have been released athttps://github.com/NovaMind-Z/CSL.

The task of cross-modal image retrieval has recently attracted considerable research attention. In real-world scenarios, keyword-based queries issued by users are usually short and have broad semantics. Therefore, semantic diversity is as important as retrieval accuracy in such user-oriented services, which improves user experience. However, most typical cross-modal image retrieval methods based on single point query embedding inevitably result in low semantic diversity, while existing diverse retrieval approaches frequently lead to low accuracy due to a lack of cross-modal understanding. To address this challenge, we introduce an end-to-end solution termed variational multiple instance graph (VMIG), in which a continuous semantic space is learned to capture diverse query semantics, and the retrieval task is formulated as a multiple instance learning problems to connect diverse features across modalities. Specifically, a query-guided variational autoencoder is employed to model the continuous semantic space instead of learning a single-point embedding. Afterward, multiple instances of the image and query are obtained by sampling in the continuous semantic space and applying multihead attention, respectively. Thereafter, an instance graph is constructed to remove noisy instances and align cross-modal semantics. Finally, heterogeneous modalities are robustly fused under multiple losses. Extensive experiments on two real-world datasets have well verified the effectiveness of our proposed solution in both retrieval accuracy and semantic diversity.

With the recent boom of video-based social platforms (e.g., YouTube and TikTok), video retrieval using sentence queries has become an important demand and attracts increasing research attention. Despite the decent performance, existing text-video retrieval models in vision and language communities are impractical for large-scale Web search because they adopt brute-force search based on high-dimensional embeddings. To improve efficiency, Web search engines widely apply vector compression libraries (e.g., FAISS [26]) to post-process the learned embeddings. Unfortunately, separate compression from feature encoding degrades the robustness of representations and incurs performance decay. To pursue a better balance between performance and efficiency, we propose the first quantized representation learning method for cross-view video retrieval, namely Hybrid Contrastive Quantization (HCQ). Specifically, HCQ learns both coarse-grained and fine-grained quantizations with transformers, which provide complementary understandings for texts and videos and preserve comprehensive semantic information. By performing Asymmetric-Quantized Contrastive Learning (AQ-CL) across views, HCQ aligns texts and videos at coarse-grained and multiple fine-grained levels. This hybrid-grained learning strategy serves as strong supervision on the cross-view video quantization model, where contrastive learning at different levels can be mutually promoted. Extensive experiments on three Web video benchmark datasets demonstrate that HCQ achieves competitive performance with state-of-the-art non-compressed retrieval methods while showing high efficiency in storage and computation. Code and configurations are available at https://github.com/gimpong/WWW22-HCQ.

Text coherence plays a key role in document quality assessment. Most existing text coherence methods only focus on similarity of adjacent sentences. However, local coherence exists in sentences with broader contexts and diverse rhetoric relations, rather than just adjacent sentences similarity. Besides, the highlevel text coherence is also an important aspect of document quality. To this end, we propose a hierarchical coherence model for document quality assessment. In our model, we implement a local attention mechanism to capture the location semantics, bilinear tensor layer for measure coherence and max-coherence pooling for acquiring high-level coherence. We evaluate the proposed method on two realistic tasks: news quality judgement and automated essay scoring. Experimental results demonstrate the validity and superiority of our work.

Real-time forwarding prediction for predicting online contents' popularity is beneficial to various social applications for enhancing interactive social behaviors. Cascade graphs, formed by online contents' propagation, play a vital role in real-time forwarding prediction. Existing cascade graph modeling methods are inadequate to embed cascade graphs that have hub structures and deep cascade paths, or they fail to handle the short-term outbreak of forwarding amount. To this end, we propose a novel real-time forwarding prediction method that includes an effective approach for cascade graph embedding and a short-term variation sensitive method for time-series modeling, making the best of cascade graph features. Using two real world datasets, we demonstrate the significant superiority of the proposed method compared with the state-of-the-art. Our experiments also reveal interesting implications hidden in the performance differences between cascade graph embedding and time-series modeling.

The automatic quality assessment of self-media online articles is an urgent and new issue, which is of great value to the online recommendation and search. Different from traditional and well-formed articles, self-media online articles are mainly created by users, which have the appearance characteristics of different text levels and multi-modal hybrid editing, along with the potential characteristics of diverse content, different styles, large semantic spans and good interactive experience requirements. To solve these challenges, we establish a joint model CoQAN in combination with the layout organization, writing characteristics and text semantics, designing different representation learning subnetworks, especially for the feature learning process and interactive reading habits on mobile terminals. It is more consistent with the cognitive style of expressing an expert's evaluation of articles. We have also constructed a large scale real-world assessment dataset. Extensive experimental results show that the proposed framework significantly outperforms state-of-the-art methods, and effectively learns and integrates different factors of the online article quality assessment.

Predicting the popularity of online article sheds light to many applications such as recommendation, advertising and information retrieval. However, there are several technical challenges to be addressed for developing the best of predictive capability. (1) The popularity fluctuates under impacts of external factors, which are unpredictable and hard to capture. (2) Content and meta-data features, largely determining the online content popularity, are usually multi-modal and nontrivial to model. (3) Besides, it also needs to figure out how to integrate temporal process and content features modeling for popularity prediction in different lifecycle stages of online articles. In this paper, we propose a Deep Fusion of Temporal process and Content features (DFTC) method to tackle them. For modeling the temporal popularity process, we adopt the recurrent neural network and convolutional neural network. For multi-modal content features, we exploit the hierarchical attention network and embedding technique. Finally, a temporal attention fusion is employed for dynamically integrating all these parts. Using datasets collected from WeChat, we show that the proposed model significantly outperforms state-of-the-art approaches on popularity prediction.

Predicting users’ activity and location preferences is of great significance in location based services. Considering that users’ activity and location preferences interplay with each other, many scholars tried to figure out the relation between users’ activities and locations for improving prediction performance. However, most previous works enforce a rigid human-defined modeling strategy to capture these two factors, either activity purpose controlling location preference or spatial region determining activity preference. Unlike existing methods, we introduce spatial-activity topics as the latent factor capturing both users’ activity and location preferences. We propose Multi-task Context Aware Recurrent Neural Network to leverage the spatial activity topic for activity and location prediction. More specifically, a novel Context Aware Recurrent Unit is designed to integrate the sequential dependency and temporal regularity of spatial activity topics. Extensive experimental results based on real-world public datasets demonstrate that the proposed model significantly outperforms state-of-the-art approaches.