Collaborative filtering tries to predict the ratings of a user over some items based on opinions of other users with similar taste. The ratings are usually given in the form of a sparse matrix, the goal being to find the missing entries (i.e. ratings). Various approaches to collaborative filtering exist, some of the most popular ones being the Singular Value Decomposition (SVD) and K-means clustering. One of the challenges in the SVD approach is finding a good initialization of the unknown ratings. A possible initialization is suggested by [1]. In this paper we explain how K-means approach can be used to achieve the further refinement of this initialization for SVD. We show that our technique outperforms both initialization techniques used separately.
SVD-AE: Simple Autoencoders for Collaborative Filtering
Boeing Korea Engineering and Technology Center (BKETC)（波音韩国工程技术中心（BKETC））；Yonsei University（延世大学）；Ulsan National Institute of Science and Technology (UNIST)（蔚山国家科学技术研究院（UNIST））；Georgia Institute of Technology（佐治亚理工学院）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））
Introducing consumed items as users' implicit feedback in matrix factorization (MF) method, SVD++ is one of the most effective collaborative filtering methods for personalized recommender systems. Though powerful, SVD++ has two limitations: (i). only user-side implicit feedback is utilized, whereas item-side implicit feedback, which can also enrich item representations, is not leveraged;(ii). in SVD++, the interacted items are equally weighted when combining the implicit feedback, which can not reflect user's true preferences accurately. To tackle the above limitations, in this paper we propos…
We propose a new approach for Collaborative Filtering which is based on Boolean Matrix Factorisation (BMF) and Formal Concept Analysis. In a series of experiments on real data (Movielens dataset) we compare the approach with the SVD- and NMF-based algorithms in terms of Mean Average Error (MAE). One of the experimental consequences is that it is enough to have a binary-scaled rating data to obtain almost the same quality in terms of MAE by BMF than for the SVD-based algorithm in case of non-scaled data.
Over the years, explosive growth in the number of items in the catalog of e-commerce businesses, such as Amazon, Netflix, Pandora, etc., have warranted the development of recommender systems to guide consumers towards their desired products based on their preferences and tastes. Some of the popular approaches for building recommender systems, for mining user, derived input datasets, are: content-based systems, collaborative filtering, latent-factor systems using Singular Value Decomposition (SVD), and Restricted Boltzmann Machines (RBM). In this project, user-user collaborative filtering, item…
Graph Convolution Network (GCN) has attracted significant attention and become the most popular method for learning graph representations. In recent years, many efforts have been focused on integrating GCN into the recommender tasks and have made remarkable progress. At its core is to explicitly capture high-order connectivities between the nodes in user-item bipartite graph. However, we theoretically and empirically find an inherent drawback existed in these GCN-based recommendation methods, where GCN is directly applied to aggregate neighboring nodes will introduce noise and information redu…
Among various recommender techniques, collaborative filtering (CF) is the most successful one. And a key problem in CF is how to represent users and items. Previous works usually represent a user (an item) as a vector of latent factors (aka. \textit{embedding}) and then model the interactions between users and items based on the representations. Despite its effectiveness, we argue that it's insufficient to yield satisfactory embeddings for collaborative filtering. Inspired by the idea of SVD++ that represents users based on themselves and their interacted items, we propose a general collaborat…
Previous highly scalable one-class collaborative filtering methods such as Projected Linear Recommendation (PLRec) have advocated using fast randomized SVD to embed items into a latent space, followed by linear regression methods to learn personalized recommendation models per user. Unfortunately, naive SVD embedding methods often exhibit a popularity bias that skews the ability to accurately embed niche items. To address this, we leverage insights from Noise Contrastive Estimation (NCE) to derive a closed-form, efficiently computable "depopularized" embedding. While this method is not ideal f…
Institute for Information Technology（信息技术研究所）；National Research Council of Canada（加拿大国家研究委员会）
Higher-order tensor decompositions are analogous to the familiar Singular Value Decomposition (SVD), but they transcend the limitations of matrices (second-order tensors). SVD is a powerful tool that has achieved impressive results in information retrieval, collaborative filtering, computational linguistics, computational vision, and other fields. However, SVD is limited to two-dimensional arrays of data (two modes), and many potential applications have three or more modes, which require higher-order tensor decompositions. This paper evaluates four algorithms for higher-order tensor decomposit…
Many Collaborative Filtering (CF) algorithms are item-based in the sense that they analyze item-item relations in order to produce item similarities. Recently, several works in the field of Natural Language Processing (NLP) suggested to learn a latent representation of words using neural embedding algorithms. Among them, the Skip-gram with Negative Sampling (SGNS), also known as word2vec, was shown to provide state-of-the-art results on various linguistics tasks. In this paper, we show that item-based CF can be cast in the same framework of neural word embedding. Inspired by SGNS, we describe …
Collaborative Filtering is largely applied to personalize item recommendation but its performance is affected by the sparsity of rating data. In order to address this issue, recent systems have been developed to improve recommendation by extracting latent factors from the rating matrices, or by exploiting trust relations established among users in social networks. In this work, we are interested in evaluating whether other sources of preference information than ratings and social ties can be used to improve recommendation performance. Specifically, we aim at testing whether the integration of …
A recommender system, also known as a recommendation system, is a type of information filtering system that attempts to forecast a user's rating or preference for an item. This article designs and implements a complete movie recommendation system prototype based on the Genre, Pearson Correlation Coefficient, Cosine Similarity, KNN-Based, Content-Based Filtering using TFIDF and SVD, Collaborative Filtering using TFIDF and SVD, Surprise Library based recommendation system technology. Apart from that in this paper, we present a novel idea that applies machine learning techniques to construct a cl…
State-of-the-art music recommender systems are based on collaborative filtering, which builds upon learning similarities between users and songs from the available listening data. These approaches inherently face the cold-start problem, as they cannot recommend novel songs with no listening history. Content-aware recommendation addresses this issue by incorporating content information about the songs on top of collaborative filtering. However, methods falling in this category rely on a shallow user/item interaction that originates from a matrix factorization framework. In this work, we introdu…
Project ATHENA aims to develop an application to address information overload, primarily focused on Recommendation Systems (RSs) with the personalization and user experience design of a modern system. Two machine learning (ML) algorithms were used: (1) TF-IDF for Content-based filtering (CBF); (2) Classification with Matrix Factorization- Singular Value Decomposition(SVD) applied with Collaborative filtering (CF) and mean (normalization) for prediction accuracy of the CF. Data sampling in academic Research and Development of Philippine Council for Agriculture, Aquatic, and Natural Resources Re…
Structurally Refined Graph Transformer for Multimodal Recommendation
School of Computer Science, Hubei University（湖北大学计算机学院）；Hubei Key Laboratory of Big Data Intelligent Analysis and Application, Hubei University（湖北大学大数据智能分析与应用重点实验室）；Key Laboratory of Intelligent Sensing System and Security (Hubei University), Ministry of Education（智能传感系统与安全重点实验室（湖北大学））；Institute of Vocational Education, Guangdong Industry Polytechnic University（广东职业技术学院教育学院）
In computer interfaces in general, especially in information retrieval tasks, it is important to be able to quickly find and retrieve information. State of the art approach, used, for example, in search engines, is not effective as it introduces losses of meanings due to context to keywords back and forth translation. Authors argue it increases the time and reduces the accuracy of information retrieval compared to what it could be in the system that employs modern information retrieval and text mining methods while presenting results in an adaptive human- computer interface where system effect…
Recommender systems are crucial tools to overcome the information overload brought about by the Internet. Rigorous tests are needed to establish to what extent sophisticated methods can improve the quality of the predictions. Here we analyse a refined correlation-based collaborative filtering algorithm and compare it with a novel spectral method for recommending. We test them on two databases that bear different statistical properties (MovieLens and Jester) without filtering out the less active users and ordering the opinions in time, whenever possible. We find that, when the distribution of u…
The recommendation systems aim to improve the user engagement by recommending appropriate personalized content to users, exploiting information about their preferences. We propose the enabler, a hybrid recommendation system which employs various machine-learning (ML) algorithms for learning an efficient combination of several recommendation algorithms and selects the best blending for a given input.Specifically, it integrates three layers, namely, the trainer which trains the underlying recommenders, the blender which determines the most efficient combination of the recommenders, and the teste…
