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A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation
Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …
understanding, research in recommendation has shifted to inventing new recommender …
Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges
Over the past two decades, a large amount of research effort has been devoted to
develo** algorithms that generate recommendations. The resulting research progress has …
develo** algorithms that generate recommendations. The resulting research progress has …
Deep learning for recommender systems: A Netflix case study
Deep learning has profoundly impacted many areas of machine learning. However, it took a
while for its impact to be felt in the field of recommender systems. In this article, we outline …
while for its impact to be felt in the field of recommender systems. In this article, we outline …
Counterfactual explainable recommendation
By providing explanations for users and system designers to facilitate better understanding
and decision making, explainable recommendation has been an important research …
and decision making, explainable recommendation has been an important research …
Tensor decomposition for signal processing and machine learning
Tensors or multiway arrays are functions of three or more indices (i, j, k,...)-similar to matrices
(two-way arrays), which are functions of two indices (r, c) for (row, column). Tensors have a …
(two-way arrays), which are functions of two indices (r, c) for (row, column). Tensors have a …
[ספר][B] Recommender systems
CC Aggarwal - 2016 - Springer
“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …
Recurrent knowledge graph embedding for effective recommendation
Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing
methods mainly rely on hand-engineered features from KGs (eg, meta paths), which …
methods mainly rely on hand-engineered features from KGs (eg, meta paths), which …
Market segmentation and travel choice prediction in Spa hotels through TripAdvisor's online reviews
Customer segmentation via online reviews and ratings can assist different hotels, including
spa hotels, to better inform marketing strategy development and ensure optimal marketing …
spa hotels, to better inform marketing strategy development and ensure optimal marketing …
A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks
R Chen, Q Hua, YS Chang, B Wang, L Zhang… - IEEE …, 2018 - ieeexplore.ieee.org
In the era of big data, recommender system (RS) has become an effective information
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …
Sequential user-based recurrent neural network recommendations
Recurrent Neural Networks are powerful tools for modeling sequences. They are flexibly
extensible and can incorporate various kinds of information including temporal order. These …
extensible and can incorporate various kinds of information including temporal order. These …