Recommender systems have become an important instrument to connect people to information. Sparse, complex, and rapidly growing data presents new challenges to traditional recommendation algorithms. To overcome these challenges, various deep learning-based recommendation algorithms have been proposed. Among these, Variational AutoEncoder (VAE) -based recommendation methods stand out. VAEs are based on a flexible probabilistic framework, which is robust for data sparsity and compatible with other deep learning-based models for dealing with multimodal data. In addition, the deep generative structure of VAEs helps to perform Bayesian inference in an efficient manner. VAE-based recommendation algorithms have given rise to many novel graphical models, and they have achieved promising performance. In this article, we conduct a survey to systematically summarize recent VAE-based recommendation algorithms. Four frequently used characteristics of VAE-based recommendation algorithms are summarized, and a taxonomy of VAE-based recommendation algorithms is proposed. We also identify future research directions for, advanced perspectives on, and the application of VAEs in recommendation algorithms, to inspire future work on VAEs for recommender systems.

Traditional text similarity measurements use TF-IDF method to model text documents as term frequency vectors,and compute similarity between text documents by using cosine similarity.These methods ignore semantic information of text documents,and semantic information enhanced methods distinguish between text documents poorly because extended vectors with semantic similar terms aggravate the curse of dimensionality.This paper proposes a similarity measurement,which is based on TF-IDF method,and analyzes similarity between important terms in text documents.This approach uses NLP technology to pre-process text,and uses TF-IDF method to filter those key terms that have higher TF-IDF value than other common terms.With the proposed data structure TSWT(Term Similarity Weight Tree) and the definition of semantic similarity,this paper resolves the semantic information of those key terms to compute similarities between text documents.Finally,several K-Means clustering methods is used for evaluating performance of the new text document similarity.By comparing with TF-IDF and another the-state-of-art semantic information based similarity method,experimental results on benchmark corpus demonstrate that it can promote the evaluation metrics of F-Measure.

Sentence similarity modeling lies at the core of many natural language processing applications, and thus has received much attention. Owing to the success of word embeddings, recently, popular neural network methods achieved sentence embedding. Most of them focused on learning semantic information and modeling it as a continuous vector, yet the syntactic information of sentences has not been fully exploited. On the other hand, prior works have shown the benefits of structured trees that include syntactic information, while few methods in this branch utilized the advantages of word embeddings and another powerful technique - attention weight mechanism. This paper suggests to absorb their advantages by merging these techniques in a unified structure, dubbed as ACV-tree. Meanwhile, this paper develops a new tree kernel, known as ACVT kernel, that is tailored for sentence similarity measure based on the proposed structure. The experimental results, based on 19 widely-used STS datasets, demonstrate that our model is effective and competitive, compared against state-of-the-art models. Additionally, the experimental results validate that many attention weight mechanisms and word embedding techniques can be seamlessly integrated into our model, demonstrating the robustness and universality of our model.

Hashing is a practical approach for the approximate nearest neighbor search. Deep hashing methods, which train deep networks to generate compact and similarity-preserving binary codes for entities (e.g. images), have received lots of attention in the information retrieval community. A representative stream of deep hashing methods is triplet-based hashing that learns hashing models from triplets of data. The existing triplet-based hashing methods only consider triplets that are in the form of $(q,q^{+},q^{-})$ , where $q$ and $q^{+}$ are in the same class and $q$ and $q^{-}$ are in different classes. However, the number of possible triplets is approximately the cube of training examples, triplets used in the existing methods are only a small fraction of all possible triplets. This motivates us to develop a new triplet-based hashing method that adopts many more triplets in training phase. We propose Deep Listwise Triplet Hashing (DLTH) that introduces more triplets into batch-based training and a novel listwise triplet loss to capture the relative similarity in new triplets. This method has a pipeline of two steps. In Step 1, we propose a novel way to generate triplets from the soft class labels obtained by knowledge distillation module, where the triplets in the form of $(q,q^{+},q^{-})$ are a subset of the newly obtained triplets. In Step 2, we develop a novel listwise triplet loss to train the hashing network, which seeks to capture the relative similarity between images in triplets according to soft labels. We conduct comprehensive image retrieval experiments on four benchmark datasets. The experimental results show that the proposed method has superior performances over state-of-the-art baselines.