1,931 research outputs found
Learning to rank music tracks using triplet loss
Most music streaming services rely on automatic recommendation algorithms to
exploit their large music catalogs. These algorithms aim at retrieving a ranked
list of music tracks based on their similarity with a target music track. In
this work, we propose a method for direct recommendation based on the audio
content without explicitly tagging the music tracks. To that aim, we propose
several strategies to perform triplet mining from ranked lists. We train a
Convolutional Neural Network to learn the similarity via triplet loss. These
different strategies are compared and validated on a large-scale experiment
against an auto-tagging based approach. The results obtained highlight the
efficiency of our system, especially when associated with an Auto-pooling
layer
Ask the GRU: Multi-Task Learning for Deep Text Recommendations
In a variety of application domains the content to be recommended to users is
associated with text. This includes research papers, movies with associated
plot summaries, news articles, blog posts, etc. Recommendation approaches based
on latent factor models can be extended naturally to leverage text by employing
an explicit mapping from text to factors. This enables recommendations for new,
unseen content, and may generalize better, since the factors for all items are
produced by a compactly-parametrized model. Previous work has used topic models
or averages of word embeddings for this mapping. In this paper we present a
method leveraging deep recurrent neural networks to encode the text sequence
into a latent vector, specifically gated recurrent units (GRUs) trained
end-to-end on the collaborative filtering task. For the task of scientific
paper recommendation, this yields models with significantly higher accuracy. In
cold-start scenarios, we beat the previous state-of-the-art, all of which
ignore word order. Performance is further improved by multi-task learning,
where the text encoder network is trained for a combination of content
recommendation and item metadata prediction. This regularizes the collaborative
filtering model, ameliorating the problem of sparsity of the observed rating
matrix.Comment: 8 page
Deep content-based music recommendation
Automatic music recommendation has become an increasingly relevant problem in recent years, since a lot of music is now sold and consumed digitally. Most recommender systems rely on collaborative filtering. However, this approach suffers from the cold start problem: it fails when no usage data is available, so it is not effective for recommending new and unpopular songs. In this paper, we propose to use a latent factor model for recommendation, and predict the latent factors from music audio when they cannot be obtained from usage data. We compare a traditional approach using a bag-of-words representation of the audio signals with deep convolutional neural networks, and evaluate the predictions quantitatively and qualitatively on the Million Song Dataset. We show that using predicted latent factors produces sensible recommendations, despite the fact that there is a large semantic gap between the characteristics of a song that affect user preference and the corresponding audio signal. We also show that recent advances in deep learning translate very well to the music recommendation setting, with deep convolutional neural networks significantly outperforming the traditional approach
A Review of Movie Recommendation System : Limitations, Survey and Challenges
Recommendation System is a major area which is very popular and useful for people to take proper decision. It is a method that helps user to find out the information which is beneficial for the user from variety of data available. When it comes to Movie Recommendation System, recommendation is done based on similarity between users (Collaborative Filtering) or by considering particular user's activity (Content Based Filtering) which he wants to engage with. So to overcome the limitations of collaborative and content based filtering generally, combination of collaborative and content based filtering is used so that a better recommendation system can be developed. Also various similarity measures are used to find out similarity between users for recommendation. In this paper, we have reviewed different similarity measures. Various companies like face book which recommends friends, LinkedIn which recommends job, Pandora recommends music, Netflix recommends movies, Amazon recommends products etc. use recommendation system to increase their profit and also benefit their customers. This paper mainly concentrates on the brief review of the different techniques and its methods for movie recommendation, so that research in recommendation system can be explored
The state-of-the-art in personalized recommender systems for social networking
With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0
A Temporal Usage Pattern-based Tag Recommendation Approach
While social tagging can benefit Internet users managing their resources, it suffers the problems such as diverse and/or unchecked vocabulary and unwillingness to tag. Use of freely new tags and/or reuse of frequent tags have degraded coherence of corresponding resources of each tag that further frustrates people in retrieving information due to cognitive dissonance. Tag recommender systems can recommend users the most relevant tags to the resource they intend to annotate, and drastically transfer the tagging process from generation to recognition to reduce user’s cognitive effort and time. Prior research on tag recommendation has addressed the time-dependence issues of tags by applying a time decaying measure to determine the recurrence probability of a tag according to its recency instead of its usage pattern. In response, this study intends to propose the temporal usage pattern-based tag recommendation technique to consider the usage patterns and temporal characteristic of tags for making recommendations
Recommender systems in industrial contexts
This thesis consists of four parts: - An analysis of the core functions and
the prerequisites for recommender systems in an industrial context: we identify
four core functions for recommendation systems: Help do Decide, Help to
Compare, Help to Explore, Help to Discover. The implementation of these
functions has implications for the choices at the heart of algorithmic
recommender systems. - A state of the art, which deals with the main techniques
used in automated recommendation system: the two most commonly used algorithmic
methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization
methods are detailed. The state of the art presents also purely content-based
methods, hybridization techniques, and the classical performance metrics used
to evaluate the recommender systems. This state of the art then gives an
overview of several systems, both from academia and industry (Amazon, Google
...). - An analysis of the performances and implications of a recommendation
system developed during this thesis: this system, Reperio, is a hybrid
recommender engine using KNN methods. We study the performance of the KNN
methods, including the impact of similarity functions used. Then we study the
performance of the KNN method in critical uses cases in cold start situation. -
A methodology for analyzing the performance of recommender systems in
industrial context: this methodology assesses the added value of algorithmic
strategies and recommendation systems according to its core functions.Comment: version 3.30, May 201
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