Tumblr Blog Recommendation with Boosted Inductive Matrix Completion

Shin, Donghyuk; Cetintas, Suleyman; Lee, Kuang-Chih; Dhillon, Inderjit S.

doi:10.1145/2806416.2806578

Cited by 32 publications

(24 citation statements)

References 28 publications

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“…Similarity [62], [31], [24], [124], [6], [115], [1], [15], [30] Linear [81], [72], [71], [37], [120], [35], [26] Bilinear [99], [63], [4], [96] [111], [18], [78], [109], [52], [74], [67], [22] GMF Multiple Matrix Factorization [89], [98], [91], [70], [113], [25], [12], [88], [28], [29], [13] Deep Neural Networks [60], [106], [117], [105], [23], [61] GF TF [102], [50], [41], [40], [51] FM …”

Section: Dmfmentioning

confidence: 99%

Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification

et al. 2020

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A ribute-aware CF models aims at rating prediction given not only the historical rating from users to items, but also the information associated with users (e.g. age), items (e.g. price), or even ratings (e.g. rating time). is paper surveys works in the past decade developing a ribute-aware CF systems, and discovered that mathematically they can be classi ed into four di erent categories. We provide the readers not only the high level mathematical interpretation of the existing works in this area but also the mathematical insight for each category of models. Finally we provide in-depth experiment results comparing the e ectiveness of the major works in each category.

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Section: Dmfmentioning

confidence: 99%

Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification

et al. 2020

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“…Word embedding is a language model that represents the semantic of each word using a Euclidean vector and is a well established concept in the field of natural language processing [9,10,13]. In recent years, word embeddings have also started being applied in recommender systems [11,16,23].…”

Section: The Function-function Modulementioning

confidence: 99%

Intelligently recommending key bindings on physical keyboards with demonstrations in Emacs

Zhong

2019

Proceedings of the 24th International Conference on Intelligent User Interfaces

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Physical keyboards have been peripheral input devices to electronic computers since early 1970s and become ubiquitous during the past few decades, especially in professional areas such as software programming, professional game playing, and document processing. In these real-world applications, key bindings, a fundamental vehicle for human to interact with software systems using physical keyboards, play a critical role in users' productivity. However, as essential applications of artificial intelligence research, research on intelligent user interfaces and recommender systems barely relates to key bindings on physical keyboards. In this paper, we develop a recommender system (referred to as EKBRS) for intelligently recommending key bindings with demonstration in Emacs, which we use as a base user interface. This is a brand new direction of intelligent user interface research and also a novel application of recommender systems. To the best of our knowledge, this is the world's first intelligent user interface that heavily exploits key bindings of physical keyboards and the world's first recommender system for recommending key bindings. We empirically show the effectiveness of our recommender system and briefly discuss the applicability of this recommender system to other software systems.

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“…Globally, the number of edges in a social network grows superlinearly with its number of nodes, and the average path length shrinks with the addition of new nodes [34], after an initial expansion phase [1]. The regular patterns that drive the link creation process have enabled the development of accurate methods for link prediction and recommendation [28] based on either local [35] or global structural information [9,8,45]. Fine-grained temporal traces of user activity in online social platforms opened up new avenues to investigate in detail the impact of time on network growth [60].…”

Section: Related Workmentioning

confidence: 99%

Evolution of Ego-networks in Social Media with Link Recommendations

Aiello

Barbieri²

2017

Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

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Ego-networks are fundamental structures in social graphs, yet the process of their evolution is still widely unexplored. In an online context, a key question is how link recommender systems may skew the growth of these networks, possibly restraining diversity. To shed light on this matter, we analyze the complete temporal evolution of 170M ego-networks extracted from Flickr and Tumblr, comparing links that are created spontaneously with those that have been algorithmically recommended. We find that the evolution of ego-networks is bursty, community-driven, and characterized by subsequent phases of explosive diameter increase, slight shrinking, and stabilization. Recommendations favor popular and well-connected nodes, limiting the diameter expansion. With a matching experiment aimed at detecting causal relationships from observational data, we find that the bias introduced by the recommendations fosters global diversity in the process of neighbor selection. Last, with two link prediction experiments, we show how insights from our analysis can be used to improve the effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl

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Tumblr Blog Recommendation with Boosted Inductive Matrix Completion

Cited by 32 publications

References 28 publications

Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification

Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification

Intelligently recommending key bindings on physical keyboards with demonstrations in Emacs

Evolution of Ego-networks in Social Media with Link Recommendations

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