Label enhancement (LE) is still a challenging task to mitigate the dilemma of the lack of label distribution. Existing LE work typically focuses on primarily formulating a projection between feature space and label distribution space from discriminative model perspective, which preserves the relevance consistency that the sign of recovered label distribution should be consistent with the logical label. Different from previous algorithms, we formulate this problem from a causal perspective and present a novel LE method via the structured causal model (LESCM). Specifically, the proposed LESCM deliberates establishing the causal graph with assuming that label distribution is a cause of feature and logical label, which naturally satisfies the definition of label distribution learning (LDL). With capturing the underlying causal relationships, we can significantly boost the interpretability and identifiability of label enhancement. Meanwhile, except for the relevance consistency, LESCM are encouraged to sustain the order consistency that assigns higher description degree of the recovered label distribution to the positive labels, as compared with the negative labels. Empirically, sufficient experiments on several label distribution learning data sets validate the effectiveness of LESCM.

Label distribution learning (LDL) is a novel machine learning paradigm that can be seen as an extension of multi-label learning (MLL). Compared with MLL, the advantages of LDL are reflected in the following perspectives: (1) the label distribution gives the relevance description of each label to unknown instances in quantitative terms; (2) the distribution implicitly gives the relevance intensities relation of different labels to a particular instance in qualitative terms, i.e., the label ranking relation. All existing LDL models aim to fit the ground-truth label distribution by quantitatively minimizing the distance between distributions or maximizing the similarity between distributions, which only uses the first advantage of the label distribution but ignores the label ranking relation, which may lose some useful semantic information implied in the label distribution, thus reducing the performance of LDL. Therefore, we propose a novel algorithm to solve this problem by introducing the ranking loss function to LDL. In addition, in order to evaluate the LDL algorithms more comprehensively and verify that the ranking loss is beneficial for keeping the label ranking relation, we also introduce two popular ranking evaluation metrics for LDL. The experimental results on 13 real-world datasets validate the effectiveness of our method.

Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labelel instances. Different from single-label and multi-label annotations, label distributions describe the instance by multiple labels with different intensities and accommodate to more general scenes. Since most existing machine learning datasets merely provide logical labels, label distributions are unavailable in many real-world applications. To handle this problem, we propose two novel label enhancement methods, i.e., Label Enhancement with Sample Correlations (LESC) and generalized Label Enhancement with Sample Correlations (gLESC). More specifically, LESC employs a low-rank representation of samples in the feature space, and gLESC leverages a tensor multi-rank minimization to further investigate the sample correlations in both the feature space and label space. Benefitting from the sample correlations, the proposed methods can boost the performance of label enhancement. Extensive experiments on 14 benchmark datasets demonstrate the effectiveness and superiority of our methods.

Dating recommendation becomes a critical task since the rapid development of online dating sites and it is beneficial for users to find their ideal relationships from a large number of registered members. Different users usually have different tastes when choosing their dating partners. Therefore, it is necessary to distinguish the users personal features and preferences in dating recommendation methods. However, present approaches dont capture enough user preferences from social graph and attribute data. They also ignore user attributes, which is the complementary and consistent side information of user social graphs. In this paper, we propose a Matching Individual Preferences with Features (MIPF) model to recommend dating partners jointly using user attributes and social graphs. We aim to model user features and preferences to identify what the user has and what the user likes. We also distinguish user preferences into explicit preferences and implicit preferences. The implicit preferences are mined from social graphs, while the explicit preferences are captured from the social links. Additionally, convolutional neural networks are used to extract the latent non-linear information in user attributes. Experiments on real-world online dating datasets demonstrate our MIPF model is superior to existing methods.

Most recommendation systems focus on predicting rating or finding aspect information in reviews to understand user preferences and item properties. However, these methods ignore the effectiveness and persuasiveness of recommendation results. Consequently, explainable recommendation, namely providing recommendation results with recommendation reasons at the same time, has attracted increasing attention of researchers due to its ability in fostering transparency and trust. It is lucky that some E-commerce websites provide a kind of new interaction box called Tips and users can express their comments on items with a simple sentence. This brings us an opportunity to realize explainable recommendation. Under the supervision of two explicit feedback, namely rating and textual tips, we can implement a multi-task learning model which can provide recommendation results and generate recommendation reasons at the same time. In this paper, we propose an Encoder-Decoder and Multi-Layer Perception (MLP) based Explainable Recommendation model named EMER to simultaneously implement reason generation and rating prediction. Items title contains significant product-related information and plays an important role in grabbing users attention, so we fuse it in our model to generate recommendation reasons. Numerous experiments on benchmark datasets demonstrate that our model is superior to the state-of-the-art models.

Learning an efficient news representation is a fundamental yet important problem for many tasks. Most existing news-relevant methods only take the textual information while abandoning the visual clues from the illustrations. We argue that the textual title and tags together with the visual illustrations form the main force of a piece of news and are more efficient to express the news content. In this paper, we develop a novel framework, namely Semantic Gated Network (SGN), to integrate the news title, tags and visual illustrations to obtain an efficient joint textual-visual feature for the news, by which we can directly measure the relevance between two pieces of news. Particularly, we first harvest the tag embeddings by the proposed self-supervised classification model. Besides, news title is fed into a sentence encoder pretrained by two semantically relevant news to learn efficient contextualized word vectors. Then the feature of the news title is extracted based on the learned vectors and we combine it with features of tags to obtain textual feature. Finally, we design a novel mechanism named semantic gate to adaptively fuse the textual feature and the image feature. Extensive experiments on benchmark dataset demonstrate the effectiveness of our approach.

Localizing the desired video clip for a given query in an untrimmed video has been a hot research topic for multimedia understanding. Recently, a new task named video relocalization, in which the query is a video clip, has been raised. Some methods have been developed for this task, however, these methods often require dense annotations of the temporal boundaries inside long videos for training. A more practical solution is the weakly-supervised approach, which only needs the matching information between the query and video.