Abstract
Given the glut of information on the web, it is crucially important to have a system, which will parse the information appropriately and recommend users with relevant information, this class of systems is known as Recommendation Systems (RS)-it is one of the most extensively used systems on the web today. Recently, Deep Learning (DL) models are being used to generate recommendations, as it has shown state-of-the-art (SoTA) results in the field of Speech Recognition and Computer Vision in the last decade. However, the RS is a much harder problem, as the central variable in the recommendation system’s environment is the chaotic nature of the human’s purchasing/consuming behaviors and their interest. These user-item interactions cannot be fully represented in the Euclidean-Space, as it will trivialize the interaction and undermine the implicit interactions patterns. So to preserve the implicit as well as explicit interactions of user and items, we propose a new graph based recommendation framework. The fundamental idea behind this framework is not only to generate the recommendations in the unsupervised fashion but to learn the dynamics of the graph and predict the short and long term interest of the users. In this paper, we propose the first step, a heuristic multi-layer high-dimensional graph which preserves the implicit and explicit interactions between users and items using SoTA Deep Learning models such as AutoEncoders. To generate recommendation from this generated graph a new class of neural network architecture-Graph Neural Network-can be used.
Highlights
Information overload is one of the biggest caveats in online information systems, as an average visitor might be only interested in a tiny subset of this information
Graph-based knowledge representation has been studied for decades and it has emerged as an important model for studying complex multi-relational data, the buzzword termed by Google5, Knowledge Graph [Ehrlinger and Wöß(2016)] is mostly used in industry and academia to refer this model
Latent features vector-bottle-neck layer-learned from the vectors r(i) can be represented as the latent features representations-the top layer of Figs. 3 and 4of the users u ∈ U in respect to the items i ∈ I, where the weights from the bottle-neck layer to the output layer are used as the weighted edges between the features representation layer to the users layer-top two layers in the Fig. 4
Summary
Information overload is one of the biggest caveats in online information systems, as an average visitor might be only interested in a tiny subset of this information. The terms User and Item is used to represent the visitor and product/service respectively. A matrix R, called as utility matrix, is used to represent user-item preferences in a two-dimensional sparse matrix and the data is teased out of the matrix R along with additional information to calculate recommendations. The recommendation system is no longer just a tool to save user’s time and improve user experience and important for sales of the products/services. The latent-representations learned from the utility matrix R, using Autoencoders, are embedded with the bipartite-graph, of R, to generate a high-dimensional multi-layer knowledge-graph which can be used later to generate recommendations for the user in an online manner.
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