Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Cosine-based explainable matrix factorization for collaborative filtering recommendation.

Recent years saw an explosive growth in the amount of digital information and the number of users who interact with this information through various platforms, ranging from web services to mobile applications and smart devices. This increase in information and users has naturally led to information overload which inherently limits the capacity of users to discover and find their needs among the staggering array of options available at any given time, the majority of which they may never become aware of. Online services have handled this information overload by using algorithmic filtering tools that can suggest relevant and personalized information to users. These filtering methods, known as Recommender Systems (RS), have become essential to recommend a range of relevant options in diverse domains ranging from friends, courses, music, and restaurants, to movies, books, and travel recommendations. Most research on recommender systems has focused on developing and evaluating models that can make predictions efficiently and accurately, without taking into account other desiderata such as fairness and transparency which are becoming increasingly important to establish trust with human users. For this reason, researchers have been recently pressed to develop recommendation systems that are endowed with the increased ability to explain why a recommendation is given, and hence help users make more informed decisions. Nowadays, state of the art Machine Learning (ML) techniques are being used to achieve unprecedented levels of accuracy in recommender systems. Unfortunately, most models are notorious for being black box models that cannot explain their output predictions. One such example is Matrix Factorization, a technique that is widely used in Collaborative Filtering algorithms. Unfortunately, like all black box machine learning models, MF is unable to explain its outputs. This dissertation proposes a new Cosine-based explainable Matrix Factorization model (CEMF) that incorporates a user-neighborhood explanation matrix (NSE) and incorporates a cosine based penalty in the objective function to encourage predictions that are explainable. Our evaluation experiments demonstrate that CEMF can recommend items that are more explainable and diverse compared to its competitive baselines, and that it further achieves this superior performance without sacrificing the accuracy of its predictions

Analyzing Deep Learning Algorithms for Recommender Systems

As the volume of online information increases, recommender systems have been an effective strategy to overcome information overload by giving selective recommendations based on certain criteria such as user ratings and user interactions. Recommender systems are utilized in a variety of fields, with common examples being music recommendations and product recommendations on E-Commerce websites. These systems are usually constructed using either collaborative filtering, content-based filtering, or both. The most traditional way of developing a collaborative filtering recommender system is using matrix factorization, which works by decomposing a user-item interaction matrix into the product of two lower dimensionality rectangular matrix. However, as new technologies appear, matrix factorization is often replaced by other algorithms that could perform better than in a recommendation system. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing. These successes are made possible by deep learning algorithms’ outstanding ability to learn feature representations non-linearly. The influence of deep learning is also prevalent in recommender systems, as demonstrated by its effectiveness when applied to information retrieval and recommender research. This research project performs an analysis and implementation on variants of two deep learning algorithms, autoencoder and restricted Boltzmann machines, and how they perform in recommender systems compared to matrix factorization

Recommendation system using autoencoders

The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and easily. Recommendation systems have appeared as a solution to overcome this problem. Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem. With the idea of deep learning gaining more importance, several works have emerged to improve this type of filtering. In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. A comparison of this model with the Singular Value Decomposition is made and presented in the results section. Our experiment shows a very low Root Mean Squared Error (RMSE) value, considering that the recommendations presented to the users are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse, 0.996. The results are quite promising achieving an RMSE value of 0.029 in the first dataset and 0.010 in the second one.This research has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D UnitsProject Scope: UIDB/00319/202

AutoSeqRec: Autoencoder for Efficient Sequential Recommendation

Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users. Traditional methods typically treat users as sequences of items, overlooking the collaborative relationships among them. Graph-based methods incorporate collaborative information by utilizing the user-item interaction graph. However, these methods sometimes face challenges in terms of time complexity and computational efficiency. To address these limitations, this paper presents AutoSeqRec, an incremental recommendation model specifically designed for sequential recommendation tasks. AutoSeqRec is based on autoencoders and consists of an encoder and three decoders within the autoencoder architecture. These components consider both the user-item interaction matrix and the rows and columns of the item transition matrix. The reconstruction of the user-item interaction matrix captures user long-term preferences through collaborative filtering. In addition, the rows and columns of the item transition matrix represent the item out-degree and in-degree hopping behavior, which allows for modeling the user's short-term interests. When making incremental recommendations, only the input matrices need to be updated, without the need to update parameters, which makes AutoSeqRec very efficient. Comprehensive evaluations demonstrate that AutoSeqRec outperforms existing methods in terms of accuracy, while showcasing its robustness and efficiency.Comment: 10 pages, accepted by CIKM 202

UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering

Recommending items to potentially interested users has been an important commercial task that faces two main challenges: accuracy and explainability. While most collaborative filtering models rely on statistical computations on a large scale of interaction data between users and items and can achieve high performance, they often lack clear explanatory power. We propose UIPC-MF, a prototype-based matrix factorization method for explainable collaborative filtering recommendations. In UIPC-MF, both users and items are associated with sets of prototypes, capturing general collaborative attributes. To enhance explainability, UIPC-MF learns connection weights that reflect the associative relations between user and item prototypes for recommendations. UIPC-MF outperforms other prototype-based baseline methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain on three datasets, while also providing better transparency

GInRec: A Gated Architecture for Inductive Recommendation using Knowledge Graphs

We have witnessed increasing interest in exploiting KGs to integrate contextual knowledge in recommender systems in addition to user-item interactions, e.g., ratings. Yet, most methods are transductive, i.e., they represent instances seen during training as low-dimensionality vectors but cannot do so for unseen instances. Hence, they require heavy retraining every time new items or users are added. Conversely, inductive methods promise to solve these issues. KGs enhance inductive recommendation by offering information on item-entity relationships, whereas existing inductive methods rely purely on interactions, which makes recommendations for users with few interactions sub-optimal and even impossible for new items. In this work, we investigate the actual ability of inductive methods exploiting both the structure and the data represented by KGs. Hence, we propose GInRec, a state-of-the-art method that uses a graph neural network with relation-specific gates and a KG to provide better recommendations for new users and items than related inductive methods. As a result, we re-evaluate state-of-the-art methods, identify better evaluation protocols, highlight unwarranted conclusions from previous proposals, and showcase a novel, stronger architecture for this task. The source code is available at: https://github.com/theisjendal/kars2023-recommendation-framework