26,226 research outputs found
Inferring Networks of Substitutable and Complementary Products
In a modern recommender system, it is important to understand how products
relate to each other. For example, while a user is looking for mobile phones,
it might make sense to recommend other phones, but once they buy a phone, we
might instead want to recommend batteries, cases, or chargers. These two types
of recommendations are referred to as substitutes and complements: substitutes
are products that can be purchased instead of each other, while complements are
products that can be purchased in addition to each other.
Here we develop a method to infer networks of substitutable and complementary
products. We formulate this as a supervised link prediction task, where we
learn the semantics of substitutes and complements from data associated with
products. The primary source of data we use is the text of product reviews,
though our method also makes use of features such as ratings, specifications,
prices, and brands. Methodologically, we build topic models that are trained to
automatically discover topics from text that are successful at predicting and
explaining such relationships. Experimentally, we evaluate our system on the
Amazon product catalog, a large dataset consisting of 9 million products, 237
million links, and 144 million reviews.Comment: 12 pages, 6 figure
Multimodal music information processing and retrieval: survey and future challenges
Towards improving the performance in various music information processing
tasks, recent studies exploit different modalities able to capture diverse
aspects of music. Such modalities include audio recordings, symbolic music
scores, mid-level representations, motion, and gestural data, video recordings,
editorial or cultural tags, lyrics and album cover arts. This paper critically
reviews the various approaches adopted in Music Information Processing and
Retrieval and highlights how multimodal algorithms can help Music Computing
applications. First, we categorize the related literature based on the
application they address. Subsequently, we analyze existing information fusion
approaches, and we conclude with the set of challenges that Music Information
Retrieval and Sound and Music Computing research communities should focus in
the next years
Learning Hierarchical Review Graph Representations for Recommendation
The user review data have been demonstrated to be effective in solving
different recommendation problems. Previous review-based recommendation methods
usually employ sophisticated compositional models, such as Recurrent Neural
Networks (RNN) and Convolutional Neural Networks (CNN), to learn semantic
representations from the review data for recommendation. However, these methods
mainly capture the local dependency between neighbouring words in a word
window, and they treat each review equally. Therefore, they may not be
effective in capturing the global dependency between words, and tend to be
easily biased by noise review information. In this paper, we propose a novel
review-based recommendation model, named Review Graph Neural Network (RGNN).
Specifically, RGNN builds a specific review graph for each individual
user/item, which provides a global view about the user/item properties to help
weaken the biases caused by noise review information. A type-aware graph
attention mechanism is developed to learn semantic embeddings of words.
Moreover, a personalized graph pooling operator is proposed to learn
hierarchical representations of the review graph to form the semantic
representation for each user/item. We compared RGNN with state-of-the-art
review-based recommendation approaches on two real-world datasets. The
experimental results indicate that RGNN consistently outperforms baseline
methods, in terms of Mean Square Error (MSE)
- …