Personalized search aims at re-ranking search results with reference to users' background information. The state-of-the-art personalized search methods often consider both the short-term search interests from current session behaviors and the long-term search interests from previous session behaviors. However, sessions in real-world search scenarios are usually very short, and a large number of sessions contain only one query, which makes it difficult to model short-term search interests. Intuitively, apart from current session behaviors, some recent historical session behaviors could also contribute to the current search interests, and the influence of these behaviors typically decays over time. Based on this intuition, we propose a novel heterogeneous graph based Hawkes process to improve the effectiveness of personalized search. Specifically, we first construct a heterogeneous graph to model multiple relations between users, queries, and documents. Then, we propose a heterogeneous graph neural network based algorithm to encode the representations of users' historical search behaviors. After that, we develop a multivariate Hawkes process to capture the influence of historical search behaviors on the current search intent. Our approach can dynamically model the influence of historical behaviors in a continuous time space. Thus, both the current session behaviors and the historical session behaviors can be utilized to characterize a more accurate current search intent. We evaluate our method using three real-life datasets, and the results show that our approach significantly outperforms the state-of-the-art methods in terms of several widely-used precision metrics.

Personalized search aims to customize search results based on users’ search history. The key of personalized search is to learn the representations of users’ interests from users’ search history. The state-of-the-art personalized search methods often encode group-level features of similar users to improve personalized search. However, existing group-level feature encoding methods are sensitive to noisy users, which are often contained in real-world search data. To overcome this problem, we propose a novel approach to encode group features based on a topological data analysis technique, namely, persistent homology analysis. Such topological features are typically robust to noisy data, thus can improve the personalized search quality. To the best of our knowledge, we are the first to use topological features for improving personalized Web search. We conduct extensive experiments on two real-life datasets to evaluate the proposed approach; and the results show that our solution is significantly better than the state-of-the-art personalized search models in terms of several widely used precision measures.

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).

The task of Session-based Social Recommendation (SSR) aims to utilize the social networks to make recommendations in session-based scenarios. Existing SSR methods mainly focused on using graph networks to capture complex item transition patterns, ignoring the sequential information. Few studies combined two aspects of features to enhance session preferences, resulting in information loss. Besides, modeling the entire session that some items are invalid or repeatedly clicked will interfere with the results. In this paper, to address the information loss issue in SSR, we propose a novel Dual-Channel Attentive Network (DCAN) to leverage both sequential infor-mation and complex item transitions. Specifically, we construct one channel by a light graph attention layer to capture item transitions, and we elaborate a concise attention-based layer to build the other channel to learn sequential information. To solve the invalid or repeatedly clicked problem in the session, we introduce new self-supervised learning (SSL) learning method, which allows model learning to distinguish and discard these items. However, the effect of SSL in SSR has not been investigated yet. Besides, these studies require negative sampling, which makes its performance depend on negative sampling strategies. Then, we investigate the effect of adding existing SSL frameworks in DCAN, but it has not achieved good results. Besides, we propose a novel SSL framework that does not require negative sampling for SSR, denoted as Positive sampling SSL (PSSL). Furthermore, we combined DCAN and PSSL to make more accurate recommendations, denoted as DCAN - PSSL. Extensive experiments on three public benchmark datasets demonstrate that both DCAN and DCAN - PSSL consistently outperform the state-of-the-art models.

Graph kernels and graph neural networks (GNNs) are widely used for the classification of graph data. However, many existing graph kernels and GNNs have limited expressive power, because they cannot distinguish graphs if the classic 1-dimensional Weisfeiler-Leman (1-WL) algorithm does not distinguish them. To break the 1-WL expressiveness barrier, we propose a novel method called filtration-enhanced graph transformation, which is based on a concept from the area of topological data analysis. In a nutshell, our approach first transforms each original graph into a filtration-enhanced graph based on a certain pre-defined filtration operation, and then uses the transformed graphs as the inputs for graph kernels or GNNs. The striking feature of our approach is that it is a plug-in method and can be applied in any graph kernel and GNN to enhance their expressive power. We theoretically and experimentally demonstrate that our solutions exhibit significantly better performance than the state-of-the art solutions for graph classification tasks.