2,594 research outputs found
USING FILTERS IN TIME-BASED MOVIE RECOMMENDER SYSTEMS
On a very high level, a movie recommendation system is one which uses data about the user, data about the movie and the ratings given by a user in order to generate predictions for the movies that the user will like. This prediction is further presented to the user as a recommendation. For example, Netflix uses a recommendation system to predict movies and generate favorable recommendations for users based on their profiles and the profiles of users similar to them. In user-based collaborative filtering algorithm, the movies rated highly by the similar users of a particular user are considered as recommendations to that user. But users’ preferences vary with time, which often affects the efficacy of the recommendation, especially in a movie recommendation system. Because of the constant variation of the preferences, there has been research on using time of rating or watching the movie as a significant factor for recommendation. If time is considered as an attribute in the training phase of building a recommendation model, the model might get complex. Most of the research till now does this in the training phase, however, we study the effect of using time as a factor in the post training phase and study it further by applying a genre-based filtering mechanism on the system. Employing this in the post training phase reduces the complexity of the method and also reduces the number of irrelevant recommendations
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Recent advancements in deep neural networks for graph-structured data have
led to state-of-the-art performance on recommender system benchmarks. However,
making these methods practical and scalable to web-scale recommendation tasks
with billions of items and hundreds of millions of users remains a challenge.
Here we describe a large-scale deep recommendation engine that we developed and
deployed at Pinterest. We develop a data-efficient Graph Convolutional Network
(GCN) algorithm PinSage, which combines efficient random walks and graph
convolutions to generate embeddings of nodes (i.e., items) that incorporate
both graph structure as well as node feature information. Compared to prior GCN
approaches, we develop a novel method based on highly efficient random walks to
structure the convolutions and design a novel training strategy that relies on
harder-and-harder training examples to improve robustness and convergence of
the model. We also develop an efficient MapReduce model inference algorithm to
generate embeddings using a trained model. We deploy PinSage at Pinterest and
train it on 7.5 billion examples on a graph with 3 billion nodes representing
pins and boards, and 18 billion edges. According to offline metrics, user
studies and A/B tests, PinSage generates higher-quality recommendations than
comparable deep learning and graph-based alternatives. To our knowledge, this
is the largest application of deep graph embeddings to date and paves the way
for a new generation of web-scale recommender systems based on graph
convolutional architectures.Comment: KDD 201
Trust-Networks in Recommender Systems
Similarity-based recommender systems suffer from significant limitations, such as data sparseness and scalability. The goal of this research is to improve recommender systems by incorporating the social concepts of trust and reputation. By introducing a trust model we can improve the quality and accuracy of the recommended items. Three trust-based recommendation strategies are presented and evaluated against the popular MovieLens [8] dataset
Pairwise Quantization
We consider the task of lossy compression of high-dimensional vectors through
quantization. We propose the approach that learns quantization parameters by
minimizing the distortion of scalar products and squared distances between
pairs of points. This is in contrast to previous works that obtain these
parameters through the minimization of the reconstruction error of individual
points. The proposed approach proceeds by finding a linear transformation of
the data that effectively reduces the minimization of the pairwise distortions
to the minimization of individual reconstruction errors. After such
transformation, any of the previously-proposed quantization approaches can be
used. Despite the simplicity of this transformation, the experiments
demonstrate that it achieves considerable reduction of the pairwise distortions
compared to applying quantization directly to the untransformed data
Hybrid group recommendations for a travel service
Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations
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