The intellectual system of movies recommendations based on the collaborative filtering

The investigation deals with designing and developing of intellectual system of movies recommendations  based on the collaborative filtering using the Python software environment. In particular, the approaches (Content-based approach, Collaborative filtering, Hybrid models) in recommendatory system construction with the help of neural networks have been analyzed. It has been established that it is difficult to implement and learn the Content-based approach and it strongly depends on the subject area. Collaborative filtering is more simple in implementation, training, it is universal, but it has a flaw in the form of a «cold-start». Accordingly, the collaborative filtering has been chosen for the design and development of the intellectual system of movies recommendations. While designing a system of recommendations based on collaborative filtering, the Naive Recommendations, Recommendations based on average ratings of similar users, Recommendations based on average user ratings and similarity matrix have been described; their algorithm and their implementation using the Python software environment have been demonstrated. As a result the intellectual system of recommendations has been realized and it can offer a movie to the user according to his/her preferences

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

Hybrid Collaborative Filtering with Autoencoders

Collaborative Filtering aims at exploiting the feedback of users to provide personalised recommendations. Such algorithms look for latent variables in a large sparse matrix of ratings. They can be enhanced by adding side information to tackle the well-known cold start problem. While Neu-ral Networks have tremendous success in image and speech recognition, they have received less attention in Collaborative Filtering. This is all the more surprising that Neural Networks are able to discover latent variables in large and heterogeneous datasets. In this paper, we introduce a Collaborative Filtering Neural network architecture aka CFN which computes a non-linear Matrix Factorization from sparse rating inputs and side information. We show experimentally on the MovieLens and Douban dataset that CFN outper-forms the state of the art and benefits from side information. We provide an implementation of the algorithm as a reusable plugin for Torch, a popular Neural Network framework

Attentive Neural Architecture Incorporating Song Features For Music Recommendation

Recommender Systems are an integral part of music sharing platforms. Often the aim of these systems is to increase the time, the user spends on the platform and hence having a high commercial value. The systems which aim at increasing the average time a user spends on the platform often need to recommend songs which the user might want to listen to next at each point in time. This is different from recommendation systems which try to predict the item which might be of interest to the user at some point in the user lifetime but not necessarily in the very near future. Prediction of the next song the user might like requires some kind of modeling of the user interests at the given point of time. Attentive neural networks have been exploiting the sequence in which the items were selected by the user to model the implicit short-term interests of the user for the task of next item prediction, however we feel that the features of the songs occurring in the sequence could also convey some important information about the short-term user interest which only the items cannot. In this direction, we propose a novel attentive neural architecture which in addition to the sequence of items selected by the user, uses the features of these items to better learn the user short-term preferences and recommend the next song to the user.Comment: Accepted as a paper at the 12th ACM Conference on Recommender Systems (RecSys 18