CAME: Content- and Context-Aware Music Embedding for Recommendation

A Generalized Nesterov-Accelerated Second-Order Latent Factor Model for High-Dimensional and Incomplete Data

High-dimensional and incomplete (HDI) data are frequently encountered in big date-related applications for describing restricted observed interactions among large node sets. How to perform accurate and efficient representation learning on such HDI data is a hot yet thorny issue. A latent factor (LF) model has proven to be efficient in addressing it. However, the objective function of an LF model is nonconvex. Commonly adopted first-order methods cannot approach its second-order stationary point, thereby resulting in accuracy loss. On the other hand, traditional second-order methods are impractical for LF models since they suffer from high computational costs due to the required operations on the objective's huge Hessian matrix. In order to address this issue, this study proposes a generalized Nesterov-accelerated second-order LF (GNSLF) model that integrates twofold conceptions: 1) acquiring proper second-order step efficiently by adopting a Hessian-vector algorithm and 2) embedding the second-order step into a generalized Nesterov's acceleration (GNA) method for speeding up its linear search process. The analysis focuses on the local convergence for GNSLF's nonconvex cost function instead of the global convergence has been taken; its local convergence properties have been provided with theoretical proofs. Experimental results on six HDI data cases demonstrate that GNSLF performs better than state-of-the-art LF models in accuracy for missing data estimation with high efficiency, i.e., a second-order model can be accelerated by incorporating GNA without accuracy loss.

SMONAC: Supervised Multiobjective Negative Actor-Critic for Sequential Recommendation

Recent research shows that the sole accuracy metric may lead to the homogeneous and repetitive recommendations for users and affect the long-term user engagement. Multiobjective reinforcement learning (RL) is a promising method to achieve a good balance in multiple objectives, including accuracy, diversity, and novelty. However, it has two deficiencies: neglecting the updating of negative action values and limited regulation from the RL Q-networks to the (self-)supervised learning recommendation network. To address these disadvantages, we develop the supervised multiobjective negative actor-critic (SMONAC) algorithm, which includes a negative action update mechanism and multiobjective actor-critic mechanism. For the negative action update mechanism, several negative actions are randomly sampled during each time updating, and then, the offline RL approach is utilized to learn their values. For the multiobjective actor-critic mechanism, accuracy, diversity, and novelty values are integrated into the scalarized value, which is used to criticize the supervised learning recommendation network. The comparative experiments are conducted on two real-world datasets, and the results demonstrate that the developed SMONAC achieves tremendous performance promotion, especially for the metrics of diversity and novelty.

CIPL: Counterfactual Interactive Policy Learning to Eliminate Popularity Bias for Online Recommendation

Popularity bias, as a long-standing problem in recommender systems (RSs), has been fully considered and explored for offline recommendation systems in most existing relevant researches, but very few studies have paid attention to eliminate such bias in online interactive recommendation scenarios. Bias amplification will become increasingly serious over time due to the existence of feedback loop between the user and the interactive system. However, existing methods have only investigated the causal relations among different factors statically without considering temporal dependencies inherent in the online interactive recommendation system, making them difficult to be adapted to online settings. To address these problems, we propose a novel counterfactual interactive policy learning (CIPL) method to eliminate popularity bias for online recommendation. It first scrutinizes the causal relations in the interactive recommender models and formulates a novel temporal causal graph (TCG) to guide the training and counterfactual inference of the causal interactive recommendation system. Concretely, TCG is used to estimate the causal relations of item popularity on prediction score when the user interacts with the system at each time during model training. Besides, it is also used to remove the negative effect of popularity bias in the test stage. To train the causal interactive recommendation system, we formulated our CIPL by the actor-critic framework with an online interactive environment simulator. We conduct extensive experiments on three public benchmarks and the experimental results demonstrate that our proposed method can achieve the new state-of-the-art performance.

SIIR: Symmetrical Information Interaction Modeling for News Recommendation

Accurate matching between user and candidate news plays a fundamental role in news recommendation. Most existing studies capture fine-grained user interests through effective user modeling. Nevertheless, user interest representations are often extracted from multiple history news items, while candidate news representations are learned from specific news items. The asymmetry of information density causes invalid matching of user interests and candidate news, which severely affects the click-through rate prediction for specific candidate news. To resolve the problems mentioned above, we propose a symmetrical information interaction modeling for news recommendation (SIIR) in this article. We first design a light interactive attention network for user (LIAU) modeling to extract user interests related to the candidate news and reduce interference of noise effectively. LIAU overcomes the shortcomings of complex structure and high training costs of conventional interaction-based models and makes full use of domain-specific interest tendencies of users. We then propose a novel heterogeneous graph neural network (HGNN) to enhance candidate news representation through the potential relations among news. HGNN builds a candidate news enhancement scheme without user interaction to further facilitate accurate matching with user interests, which mitigates the cold-start problem effectively. Experiments on two realistic news datasets, i.e., MIND and Adressa, demonstrate that SIIR outperforms the state-of-the-art (SOTA) single-model methods by a large margin.

Routing User-Interest Markov Tree for Scalable Personalized Knowledge-Aware Recommendation

To facilitate more accurate and explainable recommendation, it is crucial to incorporate side information into user-item interactions. Recently, knowledge graph (KG) has attracted much attention in a variety of domains due to its fruitful facts and abundant relations. However, the expanding scale of real-world data graphs poses severe challenges. In general, most existing KG-based algorithms adopt exhaustively hop-by-hop enumeration strategy to search all the possible relational paths, this manner involves extremely high-cost computations and is not scalable with the increase of hop numbers. To overcome these difficulties, in this article, we propose an end-to-end framework Knowledge-tree-routed UseR-Interest Trajectories Network (KURIT-Net). KURIT-Net employs the user-interest Markov trees (UIMTs) to reconfigure a recommendation-based KG, striking a good balance for routing knowledge between short-distance and long-distance relations between entities. Each tree starts from the preferred items for a user and routes the association reasoning paths along the entities in the KG to provide a human-readable explanation for model prediction. KURIT-Net receives entity and relation trajectory embedding (RTE) and fully reflects potential interests of each user by summarizing all reasoning paths in a KG. Besides, we conduct extensive experiments on six public datasets, our KURIT-Net significantly outperforms state-of-the-art approaches and shows its interpretability in recommendation.

G3SR: Global Graph Guided Session-Based Recommendation

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendations under the condition that user profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each session individually and try to capture user interests within a session. Despite their encouraging results, these models can only perceive intra-session items and cannot draw upon the massive historical relational information. To solve this problem, we propose a novel method named global graph guided session-based recommendation (G3SR). G3SR decomposes the session-based recommendation workflow into two steps. First, a global graph is built upon all session data, from which the global item representations are learned in an unsupervised manner. Then, these representations are refined on session graphs under the graph networks, and a readout function is used to generate session representations for each session. Extensive experiments on two real-world benchmark datasets show remarkable and consistent improvements of the G3SR method over the state-of-the-art methods, especially for cold items.

Music Recommendation via Hypergraph Embedding

In recent years, we have witnessed an ever wider spread of multimedia streaming platforms (e.g., Netflix, Spotify, and Amazon). Hence, it has become more and more essential to provide such systems with advanced recommendation facilities, in order to support users in browsing these massive collections of multimedia data according to their preferences and needs. In this context, the modeling of entities and their complex relationships (e.g., users listening to topic-based songs or authors creating different releases of their lyrics) represents the key challenge to improve the recommendation and maximize the users' satisfaction. To this end, this is the first study to leverage the high representative power of hypergraph data structures in combination with modern graph machine learning techniques in the context of music recommendation. Specifically, we propose hypergraph embeddings for music recommendation (HEMR), a novel framework for song recommendation based on hypergraph embedding. The hypergraph data model allows us to represent seamlessly all the possible and complex interactions between users and songs with the related characteristics; meanwhile, embedding techniques provide a powerful way to infer the user-song similarities by vector mapping. We have experimented the effectiveness and efficiency of our approach with respect to the state-of-the-art most recent music recommender systems, exploiting the Million Song dataset. The results show that HEMR significantly outperforms other state-of-the-art techniques, especially in scenarios where the cold-start problem arises, thus making our system a suitable solution to embed within a music streaming platform.

Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention

Recommender systems are important approaches for dealing with the information overload problem in the big data era, and various kinds of auxiliary information, including time and sequential information, can help improve the performance of retrieval and recommendation tasks. However, it is still a challenging problem how to fully exploit such information to achieve high-quality recommendation results and improve users' experience. In this work, we present a novel sequential recommendation model, called multivariate Hawkes process embedding with attention (MHPE-a), which combines a temporal point process with the attention mechanism to predict the items that the target user may interact with according to her/his historical records. Specifically, the proposed approach MHPE-a can model users' sequential patterns in their temporal interaction sequences accurately with a multivariate Hawkes process. Then, we perform an accurate sequential recommendation to satisfy target users' real-time requirements based on their preferences obtained with MHPE-a from their historical records. Especially, an attention mechanism is used to leverage users' long/short-term preferences adaptively to achieve an accurate sequential recommendation. Extensive experiments are conducted on two real-world datasets (lastfm and gowalla), and the results show that MHPE-a achieves better performance than state-of-the-art baselines.

Analysis of Data Interaction Process Based on Data Mining and Neural Network Topology Visualization

This paper addresses data mining and neural network model construction and analysis to design a data interaction process model based on data mining and topology visualization. This paper performs preprocessing data operations such as data filtering and cleaning of the collected data. A typical multichannel convolutional neural network (MCNN) in deep learning techniques is applied to alert students' academic performance. In addition, the network topology of the CNN is optimized to improve the performance of the model. The CNN has many hyperparameters that need to be tuned to construct an optimal model that can effectively interact with the data. In this paper, we propose a method to visualize the network topology within unstable regions to address the current problem of lacking an effective way to layout the network topology into specified areas. The technique transforms the network topology layout problem within the unstable region into a circular topology diffusion problem within a convex polygon, ensuring a clear, logical topology connection, and dramatically reducing the gaps in the area, making the layout more uniform beautiful. This paper constructs a real-time data interaction model based on JSON format and database triggers using message queues for reliable delivery. A platform-based real-time data interaction solution is designed by combining the timer method with the original key. The solution designed in this paper considers the real-time accuracy, security and reliability of data interaction. It satisfies the platform's initial and newly discovered requirements for data interaction.

Context Aware Recommender Systems: A Novel Approach Based on Matrix Factorization and Contextual Bias

In the world of Big Data, a tool capable of filtering data and providing choice support is crucial. Recommender Systems have this aim. These have evolved further through the use of information that would improve the ability to suggest. Among the possible exploited information, the context is widely used in literature and leads to the definition of the Context-Aware Recommender System. This paper proposes a Context-Aware Recommender System based on the concept of embedded context. This technique has been tested on different datasets to evaluate its accuracy. In particular, the use of multiple datasets allows a deep analysis of the advantages and disadvantages of the proposed approach. The numerical results obtained are promising.

Context-Aware Recommender Systems in the Music Domain: A Systematic Literature Review

The design of recommendation algorithms aware of the user’s context has been the subject of great interest in the scientific community, especially in the music domain where contextual factors have a significant impact on the recommendations. In this type of system, the user’s contextual information can come from different sources such as the specific time of day, the user’s physical activity, and geolocation, among many others. This context information is generally obtained by electronic devices used by the user to listen to music such as smartphones and other secondary devices such as wearables and Internet of Things (IoT) devices. The objective of this paper is to present a systematic literature review to analyze recent work to date in the field of context-aware recommender systems and specifically in the domain of music recommendation. This paper aims to analyze and classify the type of contextual information, the electronic devices used to collect it, the main outstanding challenges and the possible opportunities for future research directions.