Collaborative Additional Variational Autoencoder for Top-N Recommender Systems

Joint latent factors and attributes to discover interpretable preferences in recommendation

Recommendation systems are being broadly adopted in various applications to suggest items of interest to users amidst the enormous volume of available information. And many academic libraries have implemented various recommendation technologies to attract more readers and evaluate the resource utilization. And collaborative filtering (CF) technologies are widely used. However, one key issue limiting the success of collaborative filtering in certain application domains is the cold-start problem. In this paper, we aim to solve this problem with side information including the profile of the readers and the information of the books. We propose three approaches: the first is a recommendation method based on readers' side information, the second one is based on the books' side information, the third one contains two methods to combine the side information and the rating information. And the experiments evaluated on the real dataset which is very sparse validate the efficiency of the methods.

Taxonomies are ubiquitous in many real-world recommendation scenarios where each item is classified into a category of a predefined hierarchical taxonomy and provide important auxiliary information for inferring user preferences. However, traditional collaborative filtering approaches have focused on user-item interactions (e.g., ratings) and neglected the impact of taxonomy information on recommendation. In this paper, we present a taxonomy-aware denoising autoencoder based model which incorporates taxonomy-aware side information into denoising autoencoder based recommendation models to enhance recommendation accuracy and alleviate data sparsity and cold start problems in recommendation systems. We propose two types of taxonomic side information, namely the topological representation of tree-structured taxonomy and the statistical properties of the taxonomy. By integrating taxonomic side information, our model can learn more effective user latent vectors which are not only determined by user ratings but also rely on the taxonomy information. We conduct a comprehensive set of experiments on two real-world datasets which provide several outcomes: first, our proposed taxonomy-aware method outperforms the baseline method on RMSE metric. Next, information extracted from taxonomy can help alleviate data sparsity and cold start problems. Moreover, we conduct supplementary experiments to explore the reason why our proposed taxonomic side information improves recommendation performance.

Evaluation Of Recommendation System On A Movie Dataset

The cold start problem in recommender systems is a long-standing challenge, which requires recommending to new users (items) based on attributes without any historical interaction records. In these recommendation systems, warm users (items) have privileged collaborative signals of interaction records compared to cold start users (items), and these Collaborative Filtering (CF) signals are shown to have competing performance for recommendation. Many researchers proposed to learn the correlation between collaborative signal embedding space and the attribute embedding space to improve the cold start recommendation, in which user and item categorical attributes are available in many online platforms. However, the cold start recommendation is still limited by two embedding spaces modeling and simple assumptions of space transformation. As user-item interaction behaviors and user (item) attributes naturally form a heterogeneous graph structure, in this paper, we propose a privileged graph distillation model (PGD). The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links. The student model is composed of an entity-attribute graph without CF links. Specifically, the teacher model can learn better embeddings of each entity by injecting complex higher-order relationships from the constructed heterogeneous graph. The student model can learn the distilled output with privileged CF embeddings from the teacher embeddings. Our proposed model is generally applicable to different cold start scenarios with new user, new item, or new user-new item. Finally, extensive experimental results on the real-world datasets clearly show the effectiveness of our proposed model on different types of cold start problems, with average 6.6%, 5.6%, and 17.1% improvement over state-of-the-art baselines on three datasets, respectively.

The cold-start problem faced by recommender systems is a serious problem, mainly because of the lack of historical data for new users or items. Traditional recommendation techniques, such as collaborative filtering and content-based filtering, are prone to fail in making good recommendations under such conditions. This paper explores the use of reinforcement learning (RL) as a remedy for cold-start problems based on active learning methods and multi-armed bandit models. We propose a novel RL-based approach that learns user preferences incrementally from interaction and improves recommendations in an exploration-exploitation setting. The setting shows improved performance in personalization and user engagement compared to baseline methods. This paper also provides the first-order implications of the cold-start problem in the real world and assesses the challenges faced by industries impacted by this problem. Keywords - Recommender systems, cold-start problem, reinforcement learning, Markov decision processes, deep reinforcement learning, user modelling

Collaborative filtering and deep learning based recommendation system for cold start items

Cross Domain Recommendation Systems using Deep Learning: A Systematic Literature Review

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users’ preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold-start problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field, and group them into three categories, i.e., embedding-based methods, connection-based methods, and propagation-based methods. Also, we further subdivide each category according to the characteristics of these approaches. Moreover, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. Finally, we propose several potential research directions in this field.

Recommendation systems often face the challenge of cold-start problem where the two parties are completely new to the systems. Previous works introduce the integration of side information of items and users into the systems so that they can learn a representation of the inputs with better generalization, thus potentially solving the cold-start problem. However, using users' or items' side information brings another problem to deep learning based collaborative filtering models known as spurious correlations. In this case, deep learning models fail to adequately generalize and simply capture the idiosyncrasies of the training datasets. In short, deep learning models find a ‘short-cut’ created by the hidden bias in the datasets and make the spurious correlations between specific features and the outcome. In this work, we propose the counterfactual constraint on user/item attributes, allowing the systems to avoid hidden bias in the datasets, thus having the robustness and generalization. The goal is to discourage the disruptive distribution change in the prediction when the distribution shift occurs in the user/item attribute spaces. The proposed model compares favorably with conventional baseline on both synthetic and real-world datasets.

In the Big Data Era, recommender systems perform a fundamental role in data management and information filtering. In this context, Collaborative Filtering (CF) persists as one of the most prominent strategies to effectively deal with large datasets and is capable of offering users interesting content in a recommendation fashion. Nevertheless, it is well‐known CF recommenders suffer from data sparsity, mainly in cold‐start scenarios, substantially reducing the quality of recommendations. In the vast literature about the aforementioned topic, there are numerous solutions, in which the state‐of‐the‐art contributions are, in some sense, conditioned or associated with traditional CF methods such as Matrix Factorization (MF), that is, they rely on linear optimization procedures to model users and items into low‐dimensional embeddings. To overcome the aforementioned challenges, there has been an increasing number of studies exploring deep learning techniques in the CF context for latent factor modelling. In this research, authors conduct a systematic review focusing on state‐of‐the‐art literature on deep learning techniques applied in collaborative filtering recommendation, and also featuring primary studies related to mitigating the cold start problem. Additionally, authors considered the diverse non‐linear modelling strategies to deal with rating data and side information, the combination of deep learning techniques with traditional CF‐based linear methods, and an overview of the most used public datasets and evaluation metrics concerning CF scenarios.

Collaborative filtering (CF) is one of the most popular approaches to build a recommendation system. In this paper, we propose a hybrid collaborative filtering model based on a Makovian random walk to address the data sparsity and cold start problems in recommendation systems. More precisely, we construct a directed graph whose nodes consist of items and users, together with item content, user profile and social network information. We incorporate user's ratings into edge settings in the graph model. The model provides personalized recommendations and predictions to individuals and groups. The proposed algorithms are evaluated on MovieLens and Epinions datasets. Experimental results show that the proposed methods perform well compared with other graph-based methods, especially in the cold start case.

Structured, semi-structured and unstructured data is growing exponentially. The exponential increase in the data resulted in providing a plethora of alternatives for items, services and products. In e-commerce, recommender systems have become the most powerful tool to avoid choice overload. People who lack effective proficiency to evaluate the various alternatives can use Recommender systems. Poor decision making is due to the exponential growth of information and the convention of new e-business services like comparison of products, auction, purchase of products etc. This paper outline the key concepts used in recommendation systems and elucidate different recommendation approaches using content based, collaborative and hybrid methods. The paper reviews the drawbacks of various recommendation methods and the possible future scope to enhance the performance of recommendation systems. Several approaches are addressed in this paper to deal with the common cold start and data sparsity problems. The paper also proposes a hybrid recommendation system, which provides a recommendation based on the score for each scenario.

KCRec: Knowledge-aware representation Graph Convolutional Network for Recommendation

Recommender systems lie at the heart of many online services such as E-commerce, social media platforms and advertising. To keep users engaged and satisfied with the displayed items, recommender systems usually use the users' historical interactions containing their interests and purchase habits to make personalised recommendations. Recently, Graph Neural Networks (GNNs) have emerged as a technique that can effectively learn representations from structured graph data. By treating the traditional user-item interaction matrix as a bipartite graph, many existing graph-based recommender systems (GBRS) have been shown to achieve state-of-the-art performance when employing GNNs. However, the existing GBRS approaches still have several limitations, which prevent the GNNs from achieving their full potential. In this work, we propose to enhance the performance of the GBRS approaches along several research directions, namely leveraging additional items and users' side information, extending the existing undirected graphs to account for social influence among users, and enhancing their underlying optimisation criterion. In the following, we describe these proposed research directions.